Clinical predictors of peritonsillar abscess in adults.
Bibliographic record
Abstract
OBJECTIVE: The primary objective was to identify clinical predictors that differentiate a peritonsillar abscess from peritonsillar cellulitis in adults. The secondary objective was to identify the prevalence of tobacco smoking behaviour in this group of patients with peritonsillitis. SETTING: The Ottawa Hospital, a tertiary care centre. METHODS: Retrospective chart review of patients diagnosed with peritonsillar abscess or peritonsillar cellulitis in the adult emergency department of The Ottawa Hospital during the years 2002 to 2004. RESULTS: The charts of 130 patients with peritonsillitis were reviewed. Patients' ages ranged from 16 to 91 years. Fifty patients were diagnosed with a peritonsillar abscess and 80 with peritonsillar cellulitis. Forty-four percent of the patients in this study admitted to active tobacco smoking behaviour. Clinical signs significantly associated with peritonsillar abscess included trismus (p < .001), uvular deviation (p < .001), and inferior displacement of the superior pole of the tonsil (p < .001) on the affected side. Pain duration was not a significant discriminative factor (p = .069). CONCLUSIONS: The diagnosis of a peritonsillar abscess is based on clinical findings. In this study, the prevalence of tobacco smoking behaviour in patients with peritonsillar disease was high. Further study is recommended to investigate the potential causal effects of tobacco smoke on the development of peritonsillar disease.
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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 | 0.003 |
| 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) | 0.002 | 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".