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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".