Exploring the incidence and etiopathogenesis of pathological yawning as adverse side effect of psychotropic drugs.
Bibliographic record
Abstract
INTRODUCTION: Yawning is a normal, stereotyped physiological event in humans and animal kingdom. When excessive (>3 per 15 minutes), it is termed as pathological yawning (PY). PY could be due to many causes but more commonly associated with side-effect of drugs, notably involving those used in psychopharmacology. Though there are isolated case reports and case-series, there are no large-scale reports of PY. This work attempted to address this lacuna. MATERIAL AND METHODS: The current work attempted to identify characteristics of PY as collated from adverse drug effect databases of Australia (Database of Adverse Event Notifications), Canada (Canada Vigilance Adverse Reaction Online Database) and the United States of America (FDA Adverse Event Reporting System - FAERS). These databases collect and provide public access to reports of adverse events related to drugs and therapeutic goods. They act as a prime pharmacovigilance tool as well as a first-line resource for healthcare professionals, researchers, and the public to monitor the safety of these products and make informed decisions. In the first week of June 2023, open access, unrestricted adverse effect of drug databases were explored, using the word "YAWNING" as the only search term for the side effect of any drug without any restrictions. The collected details of PY cases with their gender, age, reason for drug use, other concomitant complaints as well as the nature of adverse event(s) and its treatment requirements were assessed. Descriptive statistics were used. RESULT: Of the 2655 instances in USA database, 398(15%) had more than 1 suspect drug and in total 578 medications involved. The most commonly involved drugs were apomorphine, sertraline, fluoxetine and paroxetine. In all 341(12.8%) cases reported of YAWN alone or with one another sleep disorder, the most common off ending drug were fluoxetine hydrochloride. DISCUSSION AND CONCLUSION: The neural mechanism and physiology of yawning are explained. This study stresses that a health care professional, particularly mental health professionals and neurologists, should be aware of the importance of PY to deliver the best for the patients under their care. (Neuropsychopharmacol Hung 2023; 25(4): 194-205)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".