Clinical diagnosis of pneumonia, typical of experts
Bibliographic record
Abstract
BACKGROUND: Clinical diagnosis of pneumonia is a concern when a patient presents with recent cough--new or worsened--together with fever as the chief complaint. Given this presentation, the doctor would benefit from having access to software that specifies, first, what diagnostic indicators experts typically use in that diagnosis; then, upon entry of those facts, what experts' typical probability of pneumonia is in such a case; and finally, how much this probability might change upon adding the facts from chest radiography. METHODS: We specified a set of 36 hypothetical presentations of this type by patients 20-70 years of age, involving a comprehensive set of clinical-diagnostic indicators. Members of three separate expert panels independently set the probability of pneumonia in each of these cases, and also the range of possible post-radiography probabilities. A logistic function of the diagnostic indicators was fitted to the medians of the probabilities. RESULTS: The median probability of pneumonia was a joint function of the patient's age and current rate of cigarette smoking; history as to the cough's duration, the fever's maximum, dyspnea (including whether on effort only) and rigors; and physical examination as to temperature, signs of upper respiratory infection, prolongation of expiration, dullness on percussion and some auscultation findings. Non-contributory were history of wheezing, pain on inspiration, type of sputum and signs of cold or influenza. This probability function, and the post-radiography functions based on the same indicators, are accessible at http://www.evimed.ch/pneumonia. INTERPRETATION: The expert inputs to clinical diagnosis that were derived and made readily accessible provide for expertly clinical diagnosis of pneumonia, relevant for decisions about radiography and treatment without it.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 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".