Use of a Single Case-Finding Questionnaire to Simultaneously Target Multiple Related Diseases Allows Enhanced Disease Detection
Bibliographic record
Abstract
Abstract Objective To develop a research methodology to apply a single case-finding tool to multiple related diseases and to evaluate the ability of a single tool to detect two or more related chronic diseases. Methods Adults in the community with no prior history of physician-diagnosed lung disease who self-reported respiratory symptoms were contacted via random-digit dialing. Multiple risk scores, one for asthma and one for COPD, were developed using data from a single case-finding questionnaire administered to the study population. Each score was statistically optimized for targeted detection of cases having one disease in the class. External validation of tandem risk scores was prospectively conducted in an independent sample and predictive performance re-evaluated. Results Sensitivity for detection of asthma improved from 87% using single risk scores to 96% using tandem risk scores, and sensitivity for detection of COPD similarly improved from 87% to 99%. In the independent validation cohort, case-finding sensitivities increased from 64% and 59% using single risk scores to 95% and 96% using tandem risk scores for asthma and for COPD, respectively. Conclusions Use of a single questionnaire which incorporates risk scores for multiple diseases considered in tandem, rather than individually, enhances the yield of cases detected when compared with one-at-a-time application of risk scores for case discovery. Benefits include greater efficiency in case-finding and improved sensitivities for detection of each disease. What is New? We describe case finding in a population of undiagnosed, symptomatic subjects who have one disease from a class of related chronic diseases. Multiple risk scores are developed using data from a single case-finding instrument administered to a representative sample from the study population. Each score is statistically optimized for targeted detection of cases having one disease in the class. Use of multiple risk scores, when considered in tandem, enhances the yield of cases detected when compared with one-at-a-time application of risk scores for case discovery.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".