Challenges in diagnosis and health care in polycystic ovary syndrome in Canada: a patient view to improve health care
Bibliographic record
Abstract
BACKGROUND: Polycystic Ovary Syndrome (PCOS) is the most common endocrine-metabolic disorder affecting health and quality of life of those affected across the lifespan. We currently have limited evidence-based data on the experience of those living with PCOS in the health care system including diagnosis, health concerns and disease management. The aim of this study was to assess the perceptions of health status, health care experience and disease management support in those affected by PCOS in Alberta, Canada. METHODS: An online questionnaire was completed via REDCap by individuals self-reporting a diagnosis of PCOS. Question categories included demographics, symptoms of PCOS and time to confirm a diagnosis, follow-up care, health concerns, and information resources. Descriptive statistics were used and thematic analyses was applied to open-response questions. RESULTS: Responses from 194 participants living in Canada (93% in Alberta) were included. The average age was 34 ± 8 years and BMI was 35 ± 9. Menstrual irregularity was identified in 84% of respondents as the first symptom noticed and the primary reason for seeking a medical consultation. A PCOS diagnosis occurred on average 4.3 years following awareness of first symptoms and required consultation with more than one primary care provider for 57% of respondents. Half (53%) of respondents reported not receiving a referral to specialists for follow-up care and 70% were not informed about long-term health morbidity such as diabetes or cardiovascular disease. Most respondents (82%) did their own research about PCOS using on-line sources, academic literature and advice from peer support. The participant themes from open questions for improving health care included more resources and support, increased and reliable information, better education and training for clinicians, timely diagnosis, prompt referrals to specialists, and generally more compassion and empathy to the challenges faced by those managing their disease. CONCLUSION: Our findings highlight the health concerns and challenges in health care for those with PCOS. In Alberta, Canada we have identified major gaps in health care including a timely diagnosis, follow up care and supports, and multidisciplinary care. This evidence-based data can be used to inform development of pathways to improve the health care experience in those affected by PCOS.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".