Clinical Diagnosis of Hypersensitivity Pneumonitis
Bibliographic record
Abstract
The diagnosis of hypersensitivity pneumonitis (HP) is difficult and often relies on histopathology. Our objective was to identify diagnostic criteria and to develop a clinical prediction rule for this disease. Consecutive patients presenting a condition for which HP was considered in the differential diagnosis underwent a program of simple standardized diagnostic procedures. High-resolution computed tomography scan and bronchoalveolar lavage (BAL) defined the presence or absence of HP. Patients underwent surgical lung biopsy when the computed tomography scan, BAL, and other diagnostic procedures failed to yield a diagnosis. A cohort of 400 patients (116 with HP, 284 control subjects) provided data for the rule derivation. Six significant predictors of HP were identified: (1) exposure to a known offending antigen, (2) positive precipitating antibodies to the offending antigen, (3) recurrent episodes of symptoms, (4) inspiratory crackles on physical examination, (5) symptoms occurring 4 to 8 hours after exposure, (6) and weight loss. The area under the receiver operating characteristic curve was 0.93 (95% confidence interval: 0.90-0.95). The rule retained its accuracy when validated in a separate cohort of 261 patients. The diagnosis of HP can often be made or rejected with confidence, especially in areas of high or low prevalence, respectively, without BAL or biopsy.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".