[How family physicians estimate their knowledge and skills in musculoskeletal problems?].
Bibliographic record
Abstract
UNLABELLED: Musculoskeletal conditions are common reasons for consultation in primary care and constitute 14-28% of primary care visits and visits to emergency services. General practitioners [GP] diagnose and treat the majority of patients with musculoskeletal problems. Surveys conducted confirmed the discrepancy between the number of GP musculoskeLetal consultations and the amount of time spent on orthopedic and musculoskeletal teaching in undergraduate and postgraduate education in different countries. It would be considered negligent for a GP to be incompetent in assessing the function of the heart or lungs, yet it is quite common for students to leave medical school without being able to make a general assessment of the musculoskeletal system. This review analyses the forms and duration of medical teaching on musculoskeletal disorders in several parts of the world and in Israel. Some studies have investigated the current situation in the undergraduate education of musculoskeletal teaching. The recent survey by the Bone and Joint Decade of undergraduate teaching in different specialties in 32 countries considered that the average length of medical teaching time of orthopedics, rheumatology and physical medical rehabilitation is insufficient and usually emphasize surgically managed musculoskeletal problems that are not relevant for the future practice of most doctors. The surveys that investigate postgraduate training have tested the confidence of GPs in performing regional musculoskeletal examinations and management of specific conditions. They found the different levels of confidence between GPs in UK, Canada, USA (including Hawaii) and developing, countries, with the tests showing deficient knowledge and skills in assessment and treatment of musculoskeletal conditions. CONCLUSIONS: It should be the personal obligation of GPs to update themselves regularly and monitor their performance to ensure the appropriate care of musculoskeletal problems. This will be possibLe through increasing the curriculum time of studying musculoskeletal diseases to at least 6 weeks and developing a CME musculoskeletal program. Different CME Musculoskeletal programs are being established in Family Medicine departments in Israel. It is important to investigate all musculoskeletal programs and to develop the universal musculoskeletal program for postgraduate education.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".