Diagnostic and treatment concordance between a physiotherapist and an orthopedic surgeon – A pilot study
Bibliographic record
Abstract
Musculoskeletal impairments affect one-third of the adult population, are one of the major contributors to lost time from work, and account for one-third of a general practitioner's caseload. These injuries respond well to physiotherapy, but access can be limited in a publicly funded health care system. Improved access to physiotherapy occurs in a collaborative model of care in orthopedic clinics however the extent to which the patient receives similar diagnoses and treatment recommendations has not been reported. The purpose of this study was to determine diagnostic concordance and accuracy, and treatment concordance between a physiotherapist and orthopedic surgeons. Twenty-five subjects in an orthopedic clinic were assessed by a physiotherapist and an orthopedic surgeon. Diagnosis and treatment recommendations were made by each separately. These were compared for concordance between professionals and diagnostic accuracy. The physiotherapist and the orthopedic surgeon had 90% concordance in diagnoses of knee and shoulder impairments, and 75% accuracy when compared to definitive diagnostic methods. They had 87% agreement in treatment recommendations, however, the physiotherapist gave three treatment recommendations per patient where the surgeon gave two. In a collaborative care context therefore, this study suggests, that physiotherapists have similar diagnostic capabilities to orthopedic surgeons, and they will enhance the conservative treatment options offered to orthopedic patients.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".