What patients with low back pain received care in multidisciplinary therapy versus exercise therapy? Applying funder criteria
Bibliographic record
Abstract
Which treatment program a patient with low back pain (LBP) should receive is a common clinical conundrum. This thesis investigates the potential usefulness of examining routinely collected patient characteristics at baseline assessment to understanding what patients received different programs that were funded by workers’ compensation boards and auto insurers. A retrospective study design was utilized to perform a secondary analysis that examined patient information from a clinical database of a private rehabilitation provider of treatment services in Alberta and British Columbia. Regression analysis was used to examine the associations between patient-related factors and multidisciplinary or exercise treatment received. The dataset revealed that lower level of functional ability (OR Alberta 0.93, British Columbia 0.93), duration of symptoms greater than 90 days (OR Alberta 2.3, British Columbia 5.3), constant pain (OR British Columbia 2.6), being off work (OR Alberta 2.2) and WCB treatment funding (OR Alberta 5.4, British Columbia 3.8) were associated with increased odds of receiving MDT instead of ET. These results create an opportunity for stakeholders to engage and dialogue so that treatment program admission criteria can be better aligned to who receives MDT and ET.
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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 | 0.020 |
| 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 | 0.000 |
| 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 | 0.000 |
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".