Patient gender bias on the diagnosis of idiopathic pulmonary fibrosis
Bibliographic record
Abstract
BACKGROUND: Patient sex has clinical and prognostic implications in idiopathic pulmonary fibrosis (IPF). It is not known if sex-related and gender-related discrepancies exist when establishing a diagnosis of IPF. The aim was to determine how patient gender influences the diagnosis of IPF and the physician's diagnostic confidence. METHODS: This study was performed using clinical cases compiled from a single centre, then scored by respiratory physicians for a prior study. Using clinical information, physicians were asked to provide up to five diagnoses, together with their diagnostic confidence. Logistic regression was used to assess the odds of receiving a diagnosis of IPF based on patient gender. Prognostic discrimination between IPF and non-IPF was used to assess diagnostic accuracy with Cox proportional hazards modelling. RESULTS: Sixty cases were scored by 404 physicians. IPF was diagnosed more frequently in men compared with women (37.8% vs 10.6%; p<0.0001), and with greater mean diagnostic confidence (p<0.001). The odds of a male patient receiving an IPF diagnosis was greater than that of female patients, after adjusting for confounders (OR=3.05, 95% CI: 2.81 to 3.31), especially if the scan was not definite for the usual interstitial pneumonia pattern. Mortality was higher in women (HR=2.21, 95% CI: 2.02 to 2.41) than in men with an IPF diagnosis (HR=1.26, 95% CI: 1.20 to 1.33), suggesting that men were more often misclassified as having IPF. CONCLUSION: Patient gender influences diagnosis of IPF: women may be underdiagnosed and men overdiagnosed with IPF.
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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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".