A descriptive study of physiotherapist use of publicly funded diagnostic imaging modalities in Alberta, Canada
Bibliographic record
Abstract
Purpose: In 2011, physiotherapists in Alberta, Canada were authorised to refer for diagnostic imaging (DI). To date, referral patterns for DI undertaken within Alberta’s public health system by authorised physiotherapists have not been described. Methods: Descriptive study of all physiotherapist-referred publicly funded DI studies undertaken between January 2012 and December 2016 in Alberta, Canada. Data included: number of imaging studies/therapist/month, imaging modality, geographical region and body part. Descriptive statistics summarised studies across year, modality, region and body part. Yearly rates (exact 95% confidence interval) were calculated across modality and region. Results: Over the study period, 20,280 DI studies were conducted. The majority (94.1%) were performed at community imaging clinics, with the remaining 5.9% undertaken at hospitals and public health centres. X-ray (76.4%) was most common followed by ultrasound imaging (USI) (19.7%) and magnetic resonance imaging (MRI) (3.5%). Regional variation was observed with one urban centre accounting for 76.7% of studies. The annual number per physiotherapist was 31.4 referrals/year (95% CI 24.8, 38.0). The majority (99.95%) were of the musculoskeletal system including: spine (21.7%), knee (15.5%) and sacroiliac joint (12.0%) radiography. Conclusions: Physiotherapists typically request plain X-ray and USI of the musculoskeletal system, with less use of MRI.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".