How Do Patients and Otolaryngologists Define Dizziness?

OBJECTIVE

To assess for differences in how patients and otolaryngologists define the term dizziness.

METHODS

Between June 2020 and December 2022, otolaryngology clinicians and consecutive patients at 5 academic otolaryngology institutions across the United States were asked to define the term "dizziness" by completing a semantics-based questionnaire containing 20 common descriptors of the term within 5 symptom domains (imbalance-related, lightheadedness-related, motion-related, vision-related, and pain-related). The primary outcome was differences between patient and clinician perceptions of dizzy-related symptoms. Secondary outcomes included differences among patient populations by geographic location.

RESULTS

Responses were obtained from 271 patients and 31 otolaryngologists. Patients and otolaryngologists selected 7.7 ± 3.5 and 7.1 ± 4.3 symptoms, respectively. Most patients (266, 98.2%) selected from more than 1 domain and 17 (6.3%) patients identified symptoms from all 5 domains. Patients and clinicians were equally likely to define dizziness using terms from the imbalance (difference, -2.3%; 95% CI, -13.2%, 8.6%), lightheadedness (-14.1%; -29.2%, 1.0%), and motion-related (9.4; -0.3, 19.1) domains. Patients were more likely to include terms from the vision-related (23.6%; 10.5, 36.8) and pain-related (18.2%; 10.3%, 26.1%) domains. There were minor variations in how patients defined dizziness based on geographic location.

CONCLUSIONS

Patients and otolaryngologists commonly described dizziness using symptoms related to imbalance, lightheadedness, and motion. Patients were more likely to use vision or pain-related terms. Understanding of these semantic differences may enable more effective patient-clinician communication.

Scale-invariant transition probabilities in free word association trajectories

Free-word association has been used as a vehicle to understand the organization of human thoughts. The original studies relied mainly on qualitative assertions, yielding the widely intuitive notion that trajectories of word associations are structured, yet considerably more random than organized linguistic text. Here we set to determine a precise characterization of this space, generating a large number of word association trajectories in a web implemented game. We embedded the trajectories in the graph of word co-occurrences from a linguistic corpus. To constrain possible transport models we measured the memory loss and the cycling probability. These two measures could not be reconciled by a bounded diffusive model since the cycling probability was very high (16% of order-2 cycles) implying a majority of short-range associations whereas the memory loss was very rapid (converging to the asymptotic value in ∼7 steps) which, in turn, forced a high fraction of long-range associations. We show that memory loss and cycling probabilities of free word association trajectories can be simultaneously accounted by a model in which transitions are determined by a scale invariant probability distribution.