Sex-Based Disparities in Clinical Burden and Diagnostic Delay in COPD: Insights from Primary Care
Bibliographic record
Abstract
Background. Sex-based disparities in chronic obstructive pulmonary disease (COPD) diagnosis remain underexplored, particularly in primary care settings. This study assessed sex differences in clinical burden, diagnostic delay, and missed diagnostic opportunities using conventional and composite metrics. Methods. A cross-sectional analysis was conducted in 166 newly diagnosed COPD patients (76 women, 90 men) from Spanish primary care. Clinical severity, healthcare use, and diagnostic timing were compared using Mann–Whitney and chi-squared tests. Composite indices included the Symptom Intensity Score, Diagnostic Inertia Indices, DOSE Index, and Diagnosis Complexity Score. Multivariable regressions evaluated independent associations. Results. At diagnosis, women showed a greater clinical and functional burden (FEV1 % predicted: 50.4% vs. 61.4%, p < 0.001; symptom intensity z-score: 0.13 vs. −0.67, p < 0.001), higher diagnostic complexity (Diagnosis Complexity Score: 403.5 vs. 272.0, p < 0.001), and longer diagnostic delay (median: 133.0 vs. 66.5 days, p < 0.001). Stratified and composite analyses confirmed consistent sex-based asymmetries. In adjusted models, being female independently predicted a longer diagnostic delay (β = 0.888, p = 0.005), but was not significantly associated with the burden of missed diagnostic opportunities (MDOs) (β = 0.112, p = 0.395). Conclusions. Women with newly diagnosed COPD experience greater symptom burden and longer diagnostic delays. Composite metrics may improve the identification of diagnostic disparities in routine clinical settings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".