152 – NON-PHYSICIAN TRIAGE IN PATIENTS WITH LOW BACK PAIN, SCIATICA AND SPINAL STENOSIS
Bibliographic record
Abstract
Purpose: Uncertainty around back pain management results in large volumes of patients with back related complaints being referred to orthopaedic surgeons for direction. The vast majority of these referrals are non surgical leading to unacceptable wait times (T1) across Canada. This reservoir delays not only those who are disabled with problems requiring a surgical remedy but also those who only require direction to appropriate conservative care. Physiotherapists with advanced training in orthopaedics possess skills in musculoskeletal interview, exam and Orthopaedic residents on the other hand must acquire spine specific skills in interview and exam, interpretation of radiographic exams, surgical decision making as well as surgical technique in a 2–3 month residency rotation. Our question was „Can an Experienced Physiotherapist Become Proficient in Triaging for Surgically Appropriate Patients After a 2–3 month „Residency „. Method: Following a 3 month clinical residency an experienced physiotherapist and a spine surgeon independently interviewed, physically examined and reviewed diagnostic imaging of 31 patients. It was then independently concluded whether the patients were candidates for surgical treatment, required conservative management or whether further investigations were necessary to make the final determination. The level of agreement was calculated using Chance Corrected Agreement or Kappa values. Operational definitions were reviewed and a second group of 29 patients were assessed. Results: The initial Kappa score was .68 (considered good clinical agreement) and the final Kappa score was 0.84 (considered virtually interchangeable). Conclusion: A 3 month period can prepare an experienced orthopaedic physiotherapist to triage a waiting list for surgical candidates. The therapist can add value through being better prepared to direct conservative options. Expediting triage will facilitate the right person getting to the right intervention within a reasonable time frame. Addressing the backlog of referrals will also help identify the magnitude of surgical need.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".