Development and Validation of a Machine Learning Algorithm for Problematic Menopause in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN)
Bibliographic record
Abstract
Abstract Background Menopause is a normal transition in a women’s life. For some women, it is a stage without significant difficulties; for others, menopause symptoms can severely affect their quality of life. Identifying problematic menopause is essential to study the condition and to improve quality of care. This study developed and validated a case definition for problem menopause using Canadian primary care electronic medical records. Methods We used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). A case definition was developed using a reference set created by expert reviewers and a machine learning approach was applied to produce a case definition. Methods to select the most appropriate features and to re-balance our cohort were also applied. Results We randomly selected 2,776 women aged 45–60 for this analysis. An algorithm of two occurrences of ICD-9-CM code 627 in diagnosis fields within 24 months OR one occurrence of ATC code G03CA in medication fields detected problem menopause. This definition produced sensitivity 81.5% (95%CI 76.3%-85.9%), specificity of 93.5% (95%CI 91.9%-94.8%), positive predicted value 73.8% (95%CI 68.3%-78.6%), and negative predicted value 95.7% (95%CI 94.4%-96.8%). Conclusion Our case definition for problem menopause is useful for epidemiological study and demonstrated strong validity metrics. This case definition will help inform future studies exploring management of menopause in primary care 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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".