Group medical consultation for osteoporosis: a prospective pilot study of patient experience in Canadian tertiary care
Bibliographic record
Abstract
BACKGROUND: Delivery of patient-centred care is limited by physician time. Group medical consultations may save physician time without compromising patient experience. AIM: To assess patient experience and specialist physician time commitment in a group consultation for osteoporosis. DESIGN AND SETTING: Prospective pilot study at a tertiary osteoporosis centre in Canada between May 2016 and June 2019. METHOD: The authors evaluated women referred for osteoporosis who chose a 2-hour group consultation instead of a one-to-one consultation. Group consultations were led by an osteoporosis nurse and specialist physician, and consisted of individualised fracture risk assessment and education regarding osteoporosis therapies, followed by a decision-making exercise to choose a treatment plan. Patients then followed up with their GPs to implement this plan. Patient experience was assessed via a questionnaire immediately and 3 months post-consultation, at which time GP satisfaction and patient treatment status were also surveyed. RESULTS: Of 560 referrals received, 18 patients declined osteoporosis specialist assessment, 54 could not be contacted, 303 attended a one-to- one consultation, and 185 attended a group consultation. Mean participant age was 62.8 years (standard deviation [SD] 5.8) and the Fracture Risk Assessment Tool (FRAX) 10-year osteoporotic fracture risk was 13.0 (SD 7.0)%. Immediately post-consultation, 104 (97.2%) patients were satisfied and 102 (95.3%) felt included in decision making. Satisfaction was reported by 95/99 (96.0%) patients and 27/36 (75.0%) GPs. Treatment plans had been enacted by 90 (90.1%) patients. For a matched number of individual consultations, each group session conferred a specialist physician time savings of 5.5 hours. CONCLUSION: Group consultations represent a satisfactory and time-efficient alternative to one-to-one consultations for select patients with osteoporosis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".