Clinical diagnosis of acute bacterial rhinosinusitis, typical of experts
Bibliographic record
Abstract
BACKGROUND: Clinical diagnosis of acute bacterial sinusitis (ABS) is a concern when a patient presents with nasal discharge of recent onset together with facial pain or pressure. Given this presentation, the doctor would benefit from having access to software that specifies, first, what diagnostic indicators experts typically use in that diagnosis and then, upon entry of those facts, what experts' typical probability of ABS is in such a case. METHODS: We specified a set of 23 hypothetical presentations of this type by patients 20-75 years of age, involving a comprehensive set of clinical-diagnostic indicators. Members of an international expert panel independently set the probability of ABS in each of these cases. A logistic function of the diagnostic indicators was fitted to the medians of the probabilities. RESULTS: The fitting led to an expression of the experts' median probability of ABS as a joint function of the duration of the patient's facial pain/pressure, and indicators of the location(s) of this; indicators of exacerbation of the pain/pressure on bending forward, nasal obstruction, maxillary and/or frontal tenderness, pus from middle meatus, purulent postnasal drip, and fever; and indicators of recent upper respiratory tract infection, nasal polyposis and status post sinus surgery. This probability function is accessible at http://www.evimed.ch/ABS. INTERPRETATION: That probability function, made readily accessible, provides for expertly probability setting in clinical diagnosis of ABS, relevant for decisions about further diagnostics or treatment without further tests.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".