Annualized Incidence and Spectrum of Illness from an Outbreak Investigation of Bell’s Palsy
Bibliographic record
Abstract
BACKGROUND: There are limited clinical and epidemiological data on patients diagnosed with Bell's palsy. While investigating an apparent clustering of Bell's palsy, we sought to characterize the spectrum of illness in patients with this diagnosis. METHODS: A telephone survey of persons with idiopathic facial (Bell's) palsy in the Greater Toronto Area (GTA, population = 4.99 million) and Nova Scotia (population = 0.93 million) from August 1 to November 15, 1997 collected information on subject demographics, neurological symptoms, constitutional symptoms, medical investigation and management. Information regarding potential risks for exposure to infectious agents, past medical history, and family history of Bell's palsy was also collected. Subjects with other secondary causes of facial palsy were excluded. RESULTS: In the GTA and Nova Scotia, 222 and 36 patients were diagnosed with idiopathic facial (Bell's) palsy, respectively. The crude annualized incidence of Bell's palsy was 15.2 and 13.1 per 100,000 population in the GTA and Nova Scotia, respectively. There was no temporal or geographical clustering, and symptomatology did not differ significantly between the two samples. The mean age was 45 years, with 55% of subjects being female. The most common symptoms accompanying Bell's palsy were increased tearing (63%), pain in or around the ear (63%), and taste abnormalities (52%). A significant number of patients reported neurological symptoms not attributable to the facial nerve. CONCLUSION: No clustering of cases of Bell's palsy was observed to support an infectious etiology for the condition. Misdiagnosis of the etiology of facial weakness is common. Patients diagnosed with Bell's palsy have a variety of neurological symptoms, many of which cannot be attributed to a facial nerve disorder.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".