Validation of the Subgroups for Targeted Treatment for Back (STarT Back) screening tool at a tertiary care centre
Bibliographic record
Abstract
BACKGROUND: The Subgroups for Targeted Treatment for Back (STarT Back) tool is a screening questionnaire developed to identify modifiable risk factors for back pain disability in primary care. Given the ability of this tool to assist with early identification of patients at high risk, we examined its concurrent convergent and known-group construct validity in tertiary care. METHODS: This was a case-control study of adult (age > 18 yr) patients with and without an active work-related compensation claim recruited from an academic health centre between August 2017 and May 2019. Patients in the study group were assessed by a physiotherapist and an orthopedic surgeon in a spine specialty program designed to assess and treat workplace injuries. The control group included patients referred to an orthopedic spine surgeon in a publicly funded specialty clinic where an advanced practice physiotherapist determined the need for surgical consultation. We used the Roland-Morris Disability Questionnaire (RMDQ) and the Hospital Anxiety and Depression Scale (HADS) to determine the convergent and known-group construct validity of the STarT Back tool. RESULTS: = 0.002-0.001). CONCLUSION: The STarT Back tool was able to differentiate between patients with and without a compensable injury and patients with different levels of work status. The tool has acceptable convergent and known-group construct validity and can assist in clinical decision-making in a tertiary care setting where adjunct psychologic management may be indicated.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".