Association between BMI and oncologic outcomes in epithelial ovarian cancer: a predictors-matched case-control study

OBJECTIVE

We aimed to study the association between obesity and survival in ovarian cancer (OC) patients, accounting for confounders as disease stage, histology, and comorbidities.

METHODS

Retrospective matched case-control study of consecutive patients, with epithelial OC. Obese (body mass index [BMI] ≥ 35 kg m-2) patients were matched in a 1:4 ratio with patients having lower BMIs (BMI < 35 kg m-2) based on disease stage, cytoreduction state, tumor histology and ASA score. We compared the 3-year and total recurrence-free survival and overall survival through Kaplan-Meier survival curves and Cox proportional hazards.

RESULTS

Overall, 153 consecutive patients were included, of whom 32 (20.9%) had a BMI ≥ 35. and 121 a BMI < 35. The median follow-up time was 39 months (interquartile range 18-67). Both study groups were similar in multiple prognostic factors, including American Society of Anesthesiologists physical status, completion of cytoreduction, histology and stage of disease (p = 0.981, p = 0.992, p = 0.740 and p = 0.984, respectively). Ninety-five (62.1%) patients underwent robotic surgery and conversion rate from robotic to laparotomy was similar in both groups 2 (6.3%) in obese group vs. 6 (5.0%) in lower BMI patients, p = 0.673. During the follow-up time, the rate of recurrence was similar in both groups; 21 (65.6%) in obese group vs. 68 (57.1%), p = 0.387 and the rate of death events was similar; 16 (50.0%) in obese group vs. 49 (40.5%), p = 0.333). The 3-year OS was higher in the obese group (log rank p = 0.042) but the 3-year RFS was similar in both groups (log rank p = 0.556). Median total OS was similar in both groups 62 months (95% confidence interval 25-98 months) in obese vs. 67 months (95% confidence interval 15-118) in the lower BMI group, log rank p = 0.822. Median RFS was similar in both groups; 61 months (95% confidence interval 47-74) in obese, vs. 54 (95% confidence interval 43-64), log rank p = 0.842. In Cox regression analysis for OS, including obesity, age, laparotomy and neoadjuvant treatment - only neoadjuvant treatment was independently associated with longer OS: odds ratio 1.82 (95% confidence interval 1.09-3.05) and longer RFS: odds ratio 2.16 (95% confidence interval 1.37-3.41).

CONCLUSIONS

In the present study on consecutive cases of ovarian cancer, obesity did not seem to be associated with outcome, except for an apparent improved 3-year survival that faded away thereafter.

Gynecologic oncology top-cited articles: an international analysis

The aim of this paper was to study the top-cited per year (CPY) original articles published in the leading subspecialty journals in gynecologic oncology and in the leading general obstetrics and gynecology journals. We used the Web of Science and iCite databases to mine the original articles and review articles in the field of gynecologic oncology in the following journals: Gynecologic Oncology, The International Journal of Gynecological Cancer, The American Journal of Obstetrics and Gynecology and the Obstetrics & Gynecology. Top CPY articles from the four journals were analyzed and compared in a two-time point analysis. A total of 23,252 original articles and reviews were identified. The 100 Top-CPY articles were published from 1983 to 2021. Seventy (70%) in Gynecologic Oncology journal, 20 (20%) in The International Journal of Gynecological Cancer, eight (8%) in Obstetrics & Gynecology and two (2%) in The American Journal of Obstetrics and Gynecology. The most common study methodology was observational studies (20%), followed by guidelines/consensus papers (19%). The most common study topic was ovarian cancer (41%). North America originating authors composed 62% of the top CPY publications, followed by Europe (21%). The most common country of authorship was the United States (52%) followed by Canada (10%). CPY were similar in the publications before vs. after 2014 (P=.19). Study designs, study topics and continent of authorship were similar in both periods. The proportion of multi-center studies was higher after 2014 (66.6% vs. 28.8%, P=0.002) and the proportion of open access publications was higher after 2014 (66.6% vs. 15.4%, P<.001). Funded studies were more common after 2014 (75.0% vs. 53.8%, P=0.028). Ovarian cancer is the top CPY area of research in gynecologic oncology. This field is leaded by authors from the United States with multi-center studies proportion increasing in recent years. It is important to promote further high-quality research in other countries to disseminate knowledge and equality.

Performance of ChatGPT in French language Parcours d'Accès Spécifique Santé test and in OBGYN

OBJECTIVES

To evaluate the performance of ChatGPT in a French medical school entrance examination.

METHODS

A cross-sectional study using a consecutive sample of text-based multiple-choice practice questions for the Parcours d'Accès Spécifique Santé. ChatGPT answered questions in French. We compared performance of ChatGPT in obstetrics and gynecology (OBGYN) and in the whole test.

RESULTS

Overall, 885 questions were evaluated. The mean test score was 34.0% (306; maximal score of 900). The performance of ChatGPT was 33.0% (292 correct answers, 885 questions). The performance of ChatGPT was lower in biostatistics (13.3% ± 19.7%) than in anatomy (34.2% ± 17.9%; P = 0.037) and also lower than in histology and embryology (40.0% ± 18.5%; P = 0.004). The OBGYN part had 290 questions. There was no difference in the test scores and the performance of ChatGPT in OBGYN versus the whole entrance test (P = 0.76 vs P = 0.10, respectively).

CONCLUSIONS

ChatGPT answered one-third of questions correctly in the French test preparation. The performance in OBGYN was similar.

Converting to Open Access in Obstetrics and Gynaecology Journal: A Bibliometric Analysis

For various reasons, journals may convert from subscription-based to open-access (OA) publishing models, commonly referred to as flipping. In 2022, the Acta Obstetricia et Gynecologica Scandinavica flipped to OA. We performed a bibliometric analysis of authorship patterns in this journal during and after the flipping period. A total of 898 research articles were included. In the period after flipping to OA, there were more publications by authors in various countries, including from China (7.2% vs. 3.3%, P = 0.001). Accordingly, the flip to OA in a leading obstetrics and gynaecology journal seemed to impact the authorship locale.

Machine learning for prediction of concurrent endometrial carcinoma in patients diagnosed with endometrial intraepithelial neoplasia

OBJECTIVE

To identify predictive clinico-pathologic factors for concurrent endometrial carcinoma (EC) among patients with endometrial intraepithelial neoplasia (EIN) using machine learning.

METHODS

a retrospective analysis of 160 patients with a biopsy proven EIN. We analyzed the performance of multiple machine learning models (n = 48) with different parameters to predict the diagnosis of postoperative EC. The prediction variables included: parity, gestations, sampling method, endometrial thickness, age, body mass index, diabetes, hypertension, serum CA-125, preoperative histology and preoperative hormonal therapy. Python 'sklearn' library was used to train and test the models. The model performance was evaluated by sensitivity, specificity, PPV, NPV and AUC. Five iterations of internal cross-validation were performed, and the mean values were used to compare between the models.

RESULTS

Of the 160 women with a preoperative diagnosis of EIN, 37.5% (60) had a post-op diagnosis of EC. In univariable analysis, there were no significant predictors of EIN. For the five best machine learning models, all the models had a high specificity (71%-88%) and a low sensitivity (23%-51%). Logistic regression model had the highest specificity 88%, XG Boost had the highest sensitivity 51%, and the highest positive predictive value 62% and negative predictive value 73%. The highest area under the curve was achieved by the random forest model 0.646.

CONCLUSIONS

Even using the most elaborate AI algorithms, it is not possible currently to predict concurrent EC in women with a preoperative diagnosis of EIN. As women with EIN have a high risk of concurrent EC, there may be a value of surgical staging including sentinel lymph node evaluation, to more precisely direct adjuvant treatment in the event EC is identified on final pathology.

The impact of body mass index on robotic surgery outcomes in endometrial cancer

OBJECTIVES

To compare surgical outcomes of patients with endometrial cancer who underwent robotic surgery across different BMI categories.

METHODS

A retrospective study including all consecutive patients with endometrial cancer who underwent robotic surgery at a tertiary cancer center between December 2007 and December 2022. The study analyzed outcome measures, including blood loss, surgical times, length of hospitalization, perioperative complications, and conversion rates with the Kruskal-Wallis test for BMI group differences and the Chi-squared test for associations between categorical variables.

RESULTS

A total of 1329 patients with endometrial cancer were included in the study. Patients were stratified by BMI: <30.0 (n = 576; 43.3%), 30.0-39.9 (n = 449; 33.8%), and ≥ 40.0 (n = 304; 22.9%). There were no significant differences in post-anesthesia care unit (PACU) stay (p = 0.105) and hospital stay (p = 0.497) between the groups. The rate of post-op complications was similar across the groups, ranging from 8.0% to 9.5% (p = 0.761). The rate of conversion to laparotomy was also similar across the groups, ranging from 0.7% to 1.0% (p = 0.885). Women with a BMI ≥40.0 had a non-clinically relevant but greater median estimated blood loss (30 mL vs. 20 mL; p < 0.001) and longer median operating room (OR) time (288 min vs. 270 min; p < 0.001). Within the OR time, the median set-up time was longer for those with a higher BMI (58 min vs. 50 min; p < 0.001). However, skin-to-skin time (209 min vs. 203 min; p = 0.202) and post-op time (14 min vs. 13 min; p = 0.094) were comparable between groups.

CONCLUSION

BMI does not affect the peri-operative outcome of patients undergoing robotic staging procedures for endometrial cancer.

Open access transition in obstetrics and gynecology journals-The international impact

OBJECTIVE

To study the impact of converting from subscription-based publishing to open access ("flipping") in three obstetrics and gynecology (OBGYN) journals.

METHODS

We compared original articles in three OBGYN journals during a matched subscription-based and open access publishing period. We analyzed citation metrics and country of authorship.

RESULTS

Overall, 1522 studies were included; of those, 869 (57.1%) were before flipping and 653 (42.9%) were after flipping. There was a decrease in publications by lower-middle income countries from 7.7% in subscription-based publishing to 1.8% in open access (P < 0.001). There was a decrease in the proportion of articles from South Asia (2.5% vs 0.5%), North America (14.4% vs 9.4%), and the Middle East (7.4% vs 2.5%), and an increase in publications from East Asia and Pacific (17.4% vs 30.9%; P < 0.001). The relative citation ratio was higher in the open access period (median 1.65 vs 0.95, P < 0.001). The number of citations per year was higher in the open access period (median 3.0 vs 2.0, P < 0.001). There was an increase in the proportion of funded studies (from 40.2% to 47.8%; P = 0.003).

CONCLUSIONS

Flipping to open access in OBGYN journals is associated with a citation advantage with major authorship changes, leading to inequity.

It takes one to know one-Machine learning for identifying OBGYN abstracts written by ChatGPT

OBJECTIVES

To use machine learning to optimize the detection of obstetrics and gynecology (OBGYN) Chat Generative Pre-trained Transformer (ChatGPT) -written abstracts of all OBGYN journals.

METHODS

We used Web of Science to identify all original articles published in all OBGYN journals in 2022. Seventy-five original articles were randomly selected. For each, we prompted ChatGPT to write an abstract based on the title and results of the original abstracts. Each abstract was tested by Grammarly software and reports were inserted into a database. Machine-learning modes were trained and examined on the database created.

RESULTS

Overall, 75 abstracts from 12 different OBGYN journals were randomly selected. There were seven (58%) Q1 journals, one (8%) Q2 journal, two (17%) Q3 journals, and two (17%) Q4 journals. Use of mixed dialects of English, absence of comma-misuse, absence of incorrect verb forms, and improper formatting were important prediction variables of ChatGPT-written abstracts. The deep-learning model had the highest predictive performance of all examined models. This model achieved the following performance: accuracy 0.90, precision 0.92, recall 0.85, area under the curve 0.95.

CONCLUSIONS

Machine-learning-based tools reach high accuracy in identifying ChatGPT-written OBGYN abstracts.

Exploring the use of ChatGPT in OBGYN: a bibliometric analysis of the first ChatGPT-related publications

PURPOSE

Little is known about the scientific literature regarding the new revolutionary tool, ChatGPT. We aim to perform a bibliometric analysis to identify ChatGPT-related publications in obstetrics and gynecology (OBGYN).

STUDY DESIGN

A bibliometric study through PubMed database. We mined all ChatGPT-related publications using the search term "ChatGPT". Bibliometric data were obtained from the iCite database. We performed a descriptive analysis. We further compared IF among publications describing a study vs. other publications.

RESULTS

Overall, 42 ChatGPT-related publications were published across 26 different journals during 69 days. Most publications were editorials (52%) and news/briefing (22%), with only one (2%) research article identified. Five (12%) publications described a study performed. No ChatGPT-related publications in OBGYN were found. The leading journal by the number of publications was Nature (24%), followed by Lancet Digital Health and Radiology (7%, for both). The main subjects of publications were ChatGPT's scientific writing quality (26%) and a description of ChatGPT (26%) followed by tested performance of ChatGPT (14%), authorship and ethical issues (10% for both topics).In a comparison of publications describing a study performed (n = 5) vs. other publications (n = 37), mean IF was lower in the study-publications (mean 6.25 ± 0 vs. 25.4 ± 21.6, p < .001).

CONCLUSIONS

The study highlights main trends in ChatGPT-related publications. OBGYN is yet to be represented in this literature.

Analysis of Scientific Publications on the Gaza-Israeli Conflict

BACKGROUND

The Gaza-Israeli conflict poses challenges for unbiased reporting due to its complexity and media bias. We explored recent scientific publications to understand scholarly discourse and potential biases surrounding this longstanding geopolitical issue.

OBJECTIVES

To conduct a descriptive bibliometric analysis of PubMed articles regarding the recent Gaza-Israeli conflict.

METHODS

We reviewed 1628 publications using keywords and medical subject headings (MeSH) terms related to Gaza, Hamas, and Israel. We focused on articles written in English. A team of researchers assessed inclusion criteria, resolving disagreements through a third researcher.

RESULTS

Among 37 publications, Lancet, BMJ, and Nature were prominent journals. Authors from 12 countries contributed, with variety of publication types (46% correspondence, 32% news). Pro-Gaza perspectives dominated (43.2%), surpassing pro-Israel (21.6%) and neutral (35.1%) viewpoints. Pro-Gaza articles exhibited higher Altmetric scores, indicating increased social media impact. Pro-Israel publications were predominantly authored by Israelis.

CONCLUSIONS

The prevalence of pro-Gaza perspectives underscores challenges in maintaining impartiality. Higher social media impact for pro-Gaza publications emphasizes the need for nuanced examination. Addressing bias is crucial for a comprehensive understanding of this complex conflict and promoting balanced reporting.

Performance of ChatGPT in Israeli Hebrew OBGYN national residency examinations

PURPOSE

Previous studies of ChatGPT performance in the field of medical examinations have reached contradictory results. Moreover, the performance of ChatGPT in other languages other than English is yet to be explored. We aim to study the performance of ChatGPT in Hebrew OBGYN-'Shlav-Alef' (Phase 1) examination.

METHODS

A performance study was conducted using a consecutive sample of text-based multiple choice questions, originated from authentic Hebrew OBGYN-'Shlav-Alef' examinations in 2021-2022. We constructed 150 multiple choice questions from consecutive text-based-only original questions. We compared the performance of ChatGPT performance to the real-life actual performance of OBGYN residents who completed the tests in 2021-2022. We also compared ChatGTP Hebrew performance vs. previously published English medical tests.

RESULTS

In 2021-2022, 27.8% of OBGYN residents failed the 'Shlav-Alef' examination and the mean score of the residents was 68.4. Overall, 150 authentic questions were evaluated (one examination). ChatGPT correctly answered 58 questions (38.7%) and reached a failed score. The performance of Hebrew ChatGPT was lower when compared to actual performance of residents: 38.7% vs. 68.4%, p < .001. In a comparison to ChatGPT performance in 9,091 English language questions in the field of medicine, the performance of Hebrew ChatGPT was lower (38.7% in Hebrew vs. 60.7% in English, p < .001).

CONCLUSIONS

ChatGPT answered correctly on less than 40% of Hebrew OBGYN resident examination questions. Residents cannot rely on ChatGPT for the preparation of this examination. Efforts should be made to improve ChatGPT performance in other languages besides English.

Robotic surgery in obstetrics and gynecology: a bibliometric study

We aimed to identify the trends and patterns of robotic surgery research in obstetrics and gynecology since its implementation. We used data from Clarivate's Web of Science platform to identify all articles published on robotic surgery in obstetrics and gynecology. A total of 838 publications were included in the analysis. Of these, 485 (57.9%) were from North America and 281 (26.0%) from Europe. 788 (94.0%) articles originated in high-income countries and none from low-income countries. The number of publications per year reached a peak of 69 articles in 2014. The subject of 344 (41.1%) of articles was gynecologic oncology, followed by benign gynecology (n = 176, 21.0%) and urogynecology (n = 156, 18.6%). Articles discussing gynecologic oncology had lower representation in low- and middle-income countries (LMIC) (32.0% vs. 41.6%, p < 0.001) compared with high income countries. After 2015 there has been a higher representation of publications from Asia (19.7% vs. 7.7%) and from LMIC (8.4% vs. 2.6%), compared to the preceding years. In a multivariable regression analysis, journal's impact factor [aOR 95% CI 1.30 (1.16-1.41)], gynecologic oncology subject [aOR 95% CI 1.73 (1.06-2.81)] and randomized controlled trials [aOR 95% CI 3.67 (1.47-9.16)] were associated with higher number of citations per year. In conclusion, robotic surgery research in obstetrics & gynecology is dominated by research in gynecologic oncology and reached a peak nearly a decade ago. The disparity in the quantity and quality of robotic research between high income countries and LMIC raises concerns regarding the access of the latter to high quality healthcare resources such as robotic surgery.

Predictors of uncommon location of sentinel nodes in endometrial and cervical cancers

OBJECTIVE

Sentinel node mapping is widely used in the treatment of gynecologic cancers. The current study aimed to identify predictors of uncommon sentinel lymph node (SLN) locations.

METHODS

The current study included women who were operated for endometrial or cervical cancer with attempted sentinel lymph node mapping during surgical staging. Data were collected from electronic charts. The pelvis and the external ilia and obturator basins were common node locations. Para-aortic, pre-sacral, common iliac, internal iliac, and parametrial nodes were considered uncommon locations. We conducted analyses stratified according to common, uncommon, and very uncommon (para-aortic, pre-sacral, parametrial) node location sites.

RESULTS

A total of 304 women were enrolled in the current study; 15.8% had SLN in uncommon locations and 4.3% had very uncommon node locations. Body mass index (BMI) was a negative predictor for uncommon SLN locations (OR 0.88, p = 0.03). The use of either indocyanine green (ICG) or Tc99 & blue dye was an independent predictor for uncommon SLN locations (OR 8.24, p = 0.006). More recent surgeries and the presence of positive nodes were independent predictors for very uncommon node locations (OR 2.13, p = 0.011, and OR 9.3, p = 0.002, respectively).

CONCLUSIONS

BMI, tracer type, surgical year, and positive nodes were independent predictors for uncommon SLN locations. These findings suggest that surgical effort, technique and experience may result in better identification of uncommon SLN locations.

Development and validation of a machine-learning model for prediction of shoulder dystocia

OBJECTIVES

To develop a machine-learning (ML) model for prediction of shoulder dystocia (ShD) and to externally validate the model's predictive accuracy and potential clinical efficacy in optimizing the use of Cesarean delivery in the context of suspected macrosomia.

METHODS

We used electronic health records (EHR) from the Sheba Medical Center in Israel to develop the model (derivation cohort) and EHR from the University of California San Francisco Medical Center to validate the model's accuracy and clinical efficacy (validation cohort). Subsequent to application of inclusion and exclusion criteria, the derivation cohort included 686 singleton vaginal deliveries, of which 131 were complicated by ShD, and the validation cohort included 2584 deliveries, of which 31 were complicated by ShD. For each of these deliveries, we collected maternal and neonatal delivery outcomes coupled with maternal demographics, obstetric clinical data and sonographic fetal biometry. Biometric measurements and their derived estimated fetal weight were adjusted (aEFW) according to gestational age at delivery. A ML pipeline was utilized to develop the model.

RESULTS

In the derivation cohort, the ML model provided significantly better prediction than did the current clinical paradigm based on fetal weight and maternal diabetes: using nested cross-validation, the area under the receiver-operating-characteristics curve (AUC) of the model was 0.793 ± 0.041, outperforming aEFW combined with diabetes (AUC = 0.745 ± 0.044, P = 1e^-16 ). The following risk modifiers had a positive beta that was > 0.02, i.e. they increased the risk of ShD: aEFW (beta = 0.164), pregestational diabetes (beta = 0.047), prior ShD (beta = 0.04), female fetal sex (beta = 0.04) and adjusted abdominal circumference (beta = 0.03). The following risk modifiers had a negative beta that was < -0.02, i.e. they were protective of ShD: adjusted biparietal diameter (beta = -0.08) and maternal height (beta = -0.03). In the validation cohort, the model outperformed aEFW combined with diabetes (AUC = 0.866 vs 0.784, P = 0.00007). Additionally, in the validation cohort, among the subgroup of 273 women carrying a fetus with aEFW ≥ 4000 g, the aEFW had no predictive power (AUC = 0.548), and the model performed significantly better (0.775, P = 0.0002). A risk-score threshold of 0.5 stratified 42.9% of deliveries to the high-risk group, which included 90.9% of ShD cases and all cases accompanied by maternal or newborn complications. A more specific threshold of 0.7 stratified only 27.5% of the deliveries to the high-risk group, which included 63.6% of ShD cases and all those accompanied by newborn complications.

CONCLUSION

We developed a ML model for prediction of ShD and, in a different cohort, externally validated its performance. The model predicted ShD better than did estimated fetal weight either alone or combined with maternal diabetes, and was able to stratify the risk of ShD and neonatal injury in the context of suspected macrosomia. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.