Diagnosing allergic rhinitis: effectiveness of the physical examination in comparison to conventional skin testing.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of physical examination alone in diagnosing nasal allergic patients. DESIGN: A cross-sectional study of 15 consecutive evaluable patients. SETTING: A tertiary care otolaryngology clinic at the Jewish General Hospital, McGill University, Montreal. METHODS: All patients were assessed by three physicians in random order. Each conducted a specific examination, made a clinical impression, and then took a directed history to make a second impression. Both impressions were compared to skin testing. Multivariate regression analyses assessed the linear relationships of both examination and history variables to impressions and skin testing results. Fleiss kappa tests assessed interrater reliabilities. MAIN OUTCOME MEASURES: Accuracy in diagnosing allergic rhinitis by examination alone and inter-rater reliability in this diagnosis. RESULTS: When comparing physical examination variables to history variables relative to the second impression, history variables had a stronger relationship (R² = .90 vs .52). This was also true when comparing both sets of variables to skin testing (R² = .81 vs .60). The interrater reliability of physical examination variables was poorer than that of the history variables (.377 and .494, respectively). Taking all physicians, the average sensitivity, specificity, positive predictive value, and negative predictive value of the history impression were all higher than those of the examination impression. CONCLUSION: Physical examination alone yields unreliable and inconsistent results in diagnosing allergic rhinitis. This is likely secondary to the relative subjectivity involved in evaluating the nasal cavity. Adding a history to the examination is essential to increase diagnostic accuracy.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".