An evaluation of the clinical skills and experience within an orthopaedic Integrated Clinical Assessment and Treatment Service
Bibliographic record
Abstract
BACKGROUND: General practice in the UK is 'in crisis'. With 20% of GP workload relating to musculoskeletal (MSK) problems, an orthopaedic Integrated Clinical Assessment and Treatment Service (ICATS) could help support assessment of these patients in primary care, alleviating pressure on GPs. However, practitioners in ICATS must be trained appropriately to ensure its effectiveness. AIM: This evaluation aimed to identify the training levels of doctors in one Northern Ireland orthopaedic ICATS system, what their future training needs are, and suggestions for how this service could be improved to better support general practice. DESIGN & SETTING: A questionnaire study in an orthopaedic ICATS, Northern Ireland. METHOD: All seven doctors working within the Southern Trust orthopaedic ICATS were asked to complete a questionnaire detailing their training and experience in MSK medicine. Their views on how the service could be improved were elicited. RESULTS: Six of seven questionnaires were returned. All responders were Members of the Royal College of General Practitioners (MRCGP), while five of six held a Diploma in Sports and Exercise Medicine (Dip SEM). Half of responders suggested that MSK ultrasound could be beneficial within ICATS. However, it was viewed that extensive training would be required before paediatric MSK patients could be included. CONCLUSION: High levels of training and experience were reported by responders, suggesting ICATS provides a high-level MSK service. Furthermore, it was noted that inclusion of MSK ultrasound and paediatric patients into this service could be beneficial but not without undertaking further training. With appropriate funding and support the ICATS service has the potential to expand the clinical services it offers to general practice, helping to reduce work pressures in primary care at this time of crisis for UK general practice.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".