Polycystic ovary syndrome: validated questionnaire for use in diagnosis.
Bibliographic record
Abstract
OBJECTIVE: To construct and validate a questionnaire for use in diagnosis of polycystic ovary syndrome (PCOS). DESIGN: All participants completed a questionnaire, which asked clinical questions designed to assist in the diagnosis of PCOS, before their appointments with an endocrinologist. Following completion of the questionnaire, the endocrinologist (blinded to the answers) made or excluded a diagnosis of PCOS using clinical criteria and biochemical data as indicated. Questions were then evaluated for their power to predict PCOS, and a model was constructed using the most reliable items to establish a system to predict a diagnosis of PCOS. SETTING: An outpatient reproductive endocrinology clinic in Calgary, Alta. PARTICIPANTS: Adult women patients who had been referred to the clinic. Fifty patients with PCOS and 50 patients without PCOS were included in the study. MAIN OUTCOME MEASURES: Demographic information, medical history, related diagnoses, menstrual history, and fertility history. RESULTS: A history of infrequent menses, hirsutism, obesity, and acne were strongly predictive of a diagnosis of PCOS, whereas a history of failed pregnancy attempts was not useful. A history of nipple discharge outside of pregnancy strongly predicted no diagnosis of PCOS. We constructed a 4-item questionnaire for use in diagnosis of PCOS; the questionnaire yielded a sensitivity of 85% and a specificity of 85% on multivariate logistic regression and a sensitivity of 77% and a specificity of 94% using the 4-item questionnaire. Predictive accuracy was validated using a second sample of 117 patients, in addition to internal validation using bootstrap analysis. CONCLUSION: We have constructed a simple clinical tool to help diagnose PCOS. This questionnaire can be easily incorporated into family physicians' busy practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".