Investigation of polycystic ovarian syndrome: variation in practice and impact on the speed of diagnosis
Bibliographic record
Abstract
Objective Accurate diagnosis of polycystic ovarian syndrome (PCOS) enables clinical interventions/cardiometabolic risk factor management. Diagnosis can take over 2 years and multiple clinician contacts. We examined patterns of PCOS-associated biochemical investigations following initial consultation prior to pelvic ultrasound scan (USS). Methods We determined in 206 women (i) the range of different biochemical test panels used in the diagnosis of PCOS in primary/secondary care prior to USS relative to national guidance in the UK and (ii) the relation between testing patterns and time to USS to highlight potential delays introduced by inappropriate testing. Results In these 206 women, 47 different test combinations were requested at initial venepuncture; only 7 (3%) had the test panel suggested in UK guidance (follicle-stimulating hormone/luteinizing hormone/testosterone/sex hormone-binding globulin/prolactin). The number of tests performed prior to USS varied from one test to all seven tests. There was an inverse relation between the number of biochemistry tests requested at initial venepuncture episode and ‘time to scan’. Those who had <3 tests had a significantly longer time from first request to USS (median 70 days) than those with 3–7 tests (median 40 days; P = 0.002). One venepuncture episode prior to USS was associated with shorter ‘time to scan’ (median 29 days) than those with 2–4 episodes (median 255 days; P < 0.001). Conclusion There was no identifiable pattern to biochemical investigations requested as part of the initial diagnostic evaluation in women with suspected PCOS. We recommend standardization of the initial biochemical panel of analytes for PCOS workup, with incorporation into hospital/general practice ordering software systems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".