Development of claims and EHR-based algorithm for refractory chronic cough: CLeaR-CC
Bibliographic record
Abstract
Introduction: A new ICD-10 code (R05.3) was implemented for chronic cough (CC), however, this is not specific to refractory CC (RCC). Currently, there is no validated method for identifying patients with RCC using electronic records. Aim: As new medications are introduced, an algorithm to identify those with RCC will be beneficial to characterize this population in real-world (RW) settings. Methods: A targeted literature review (TLR), enhanced with semi-structured interviews with 5 international cough experts from US, UK, Canada, and Korea were used to develop the algorithm. The TLR assessed disease algorithms for CC and analogues (fibromyalgia, cluster headache, migraine, muscular dystrophy) selected due to similarities in prevalence and complex diagnoses. Results: Overall, 33 articles primarily from the US and Europe were used to develop the draft algorithm. The algorithm was further refined using output from the interviews. The revised algorithm included 2 distinct clinical pathways for RCC identification: assessment over a 2-yr period, and 3 cough events within primary care over 180 days and/or a specialist visit for RCC each year (Figure). Scenario analyses will include reducing the time periods and previously excluded comorbidities. Conclusions: The CLeaR-CC algorithm (3 cough events over 180 days and/or a specialist event for 2 consecutive yrs) will optimize the identification of patients with RCC using RW data. erj;64/suppl_68/PA2138/F1 F1 F1
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".