Addressing Gaps in Knowledge on Polycystic Ovary Syndrome in Canada
Bibliographic record
Abstract
Background: Polycystic ovary syndrome (PCOS) affects approximately 10% of the global population, including 1.4 million Canadians. Various aspects of PCOS, including its multifactorial nature and ambiguous diagnostic and treatment guidelines, may hinder optimal patient care.\nObjective: Investigate the knowledge gaps in the care of patients with PCOS in Canada.\nMethods: PubMed, EMBASE, Web of Science, and SCOPUS databases were searched, and 2098 articles were screened. After review, 23 articles discussing the work-up, clinical care, and patient experience of people with PCOS in Canada were extracted.\nResults: Four main themes prevailed in our review: 1) inconsistent and misunderstood diagnostic criteria lead to delays in diagnosis and treatment; 2) limited information provision on lifestyle management is unsupportive to patients; 3) there is an increasing need to address the psychosocial impacts of PCOS; and 4) there are opportunities to improve the experiences of women with PCOS within the health care setting.\nDiscussion: Current literature lacks Canada-wide research participation, provider perspectives, and the inclusion of sex-and-gender-based analysis. Based on our review, efforts that expedite diagnosis, personalize lifestyle guidance, attend to patients’ mental health needs, and promote positive patient experiences are avenues to improve the care of people with PCOS in Canada. The establishment of a Canadian PCOS health charity is a possible solution to help address the identified gaps in knowledge on PCOS in Canada. We propose the health charity’s framework be established on the foundational pillars of: (1) education and information; (2) professional network; (3) patient community and representation; and (4) research funding.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".