Development of a case definition for polycystic ovary syndrome using administrative health data: a validation study
Bibliographic record
Abstract
OBJECTIVES: To develop and validate a polycystic ovary syndrome (PCOS) case definition using administrative health data sources. DESIGN: A validation study. SETTING: Secondary care centre outpatient gynaecology clinic in Calgary, Alberta, Canada. PARTICIPANTS: 3951 electronic health records of women aged 18-45 years who presented to a gynaecology clinic in Calgary, Canada, between January 2014 and December 2019 were reviewed. We identified 180 patients with PCOS using the Rotterdam criteria. Participants were excluded if they were biologically male, pregnant at the time of the consultation, did not meet the date criteria or if their consultation note was missing. The chart data were connected to the Practitioner Claims and the Discharge Abstract Database by personal health number. PRIMARY AND SECONDARY OUTCOME MEASURES: Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 68 case definitions for PCOS were estimated. Case definition performance was graded. RESULTS: Of the 68 case definitions tested, none had high validity. The best performing case definitions were: (1) ≥3 instances of International Classification of Diseases-9 code 256.4 (polycystic ovaries) with exclusion codes (sensitivity 23.89%, specificity 99.59%, PPV 74.14%, NPV 96.35%) and (2) 626.X (irregular menstruation), 704.1 (hirsutism) and ≥3 instances of code 256.4 with exclusion codes (sensitivity 2.78%, specificity 99.97%, PPV 83.33%, NPV 95.40%). CONCLUSIONS: We identified several case definitions for PCOS of moderate validity with high PPV (>70%) for case ascertainment in PCOS research in jurisdictions with similar administrative health data. These case definitions are limited by low sensitivity, which should be considered when interpreting research findings.
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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 | 0.131 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 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".