A retrospective review of the community medicine needs from osteoporosis services in Canada
Bibliographic record
Abstract
Abstract Background Comprehensive, real-world osteoporosis care has many facets not explicitly addressed in practice guidelines. We sought to determine the areas of knowledge and practice needs in osteoporosis medicine for the purpose of developing an osteoporosis curriculum for specialist trainees and knowledge translation tools for primary care. Methods This was a retrospective review of referral questions received from primary care and specialists to an academic, multi-disciplinary tertiary osteoporosis and metabolic bone clinic. There were 400 referrals in each of 5 years (2015–2019) selected randomly for review. The primary referral question was elucidated and assigned to one of 16 pre-determined referral topics reflecting questions in the care of osteoporosis and metabolic bone patients. The top 7 referral topics by frequency were determined while recording the referral source. Results The majority of referrals (71%) came from urban primary care. The most common specialists to request care included rheumatology, oncology, gastroenterology and orthopedic surgery (fracture liaison services). Primary care referrals predominantly requested assistance with routine osteoporosis assessments, bisphosphonate holidays, bisphosphonate adverse effects/alternatives, fractures occurring despite therapy and adverse changes on bone densitometry despite treatment. Specialists most often referred patients with complex secondary bone diseases or cancer. The main study limitation was that knowledge needs of referring physicians were inferred from the referral question rather than tested directly. Conclusion By assessing actual community demand for services, this study identified several such topics that may be useful targets to develop high quality knowledge translation tools and curriculum design in programs training specialists in osteoporosis care.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.025 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".