French CAPTURE screening tool for undiagnosed obstructive lung disease in primary care
Bibliographic record
Abstract
Background: Obstructive lung diseases (OLD), primarily asthma and COPD are among the leading causes of morbidity and mortality worldwide. Still, they are largely underdiagnosed. Early identification of symptomatic patients would help to anticipate treatment and could improve long-term prognosis. What is the predictive accuracy of the French-translated CAPTURE tool, combining a 5-items questionnaire with a selective use of peak expiratory flow rate, for identifying undiagnosed OLD in a French primary care population? Study design and methods: Prospective multicentre study conducted in 17 primary care centres from April 2023 to October 2024. Trial was prospectively registered ( NCT05654597 ). Participants aged ≥40 years, without diagnosis of OLD were enrolled. CAPTURE was translated to French (CAPTUREF). Sensibility and specificity for undiagnosed OLD identification were measured using Martinez’s thresholds (AJRCCM, 2017). Areas under the ROC curve (AUC) for CAPTUREF and its components were calculated. Enrolment stopped before completion after a futility analysis. Results: 983/1002 participants had complete data. 60% were women, mean age was 60.2 ±11.2 years. Of the 52 (5%) participants with spirometry-defined OLD, 11 patients had a positive CAPTUREF screening. Sensitivity was 21.15% and specificity 94.74% for OLD identification. AUCs for CAPTUREF, questionnaire alone and PEF alone were 0.69, 0.68 and 0.58, respectively. A sensitivity analysis with optimal PEF thresholds (320 L/min for women, 490L/min for men) increased accuracy of CAPTUREF. Questionnaire items 1 and 5 combined with PEF achieved an AUC of 0.80. Interpretation: CAPTUREF screening tool had low sensitivity and high specificity for OLD.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".