Bibliographic record
Abstract
BACKGROUND: Tools for early identification of workers with back pain who are at high risk of adverse occupational outcome would help concentrate clinical attention on the patients who need it most, while helping reduce unnecessary interventions (and costs) among the others. This study was conducted to develop and validate clinical rules to predict the 2-year work disability status of people consulting for nonspecific back pain in primary care settings. METHODS: This was a 2-year prospective cohort study conducted in 7 primary care settings in the Quebec City area. The study enrolled 1007 workers (participation, 68.4% of potential participants expected to be eligible) aged 18-64 years who consulted for nonspecific back pain associated with at least 1 day's absence from work. The majority (86%) completed 5 telephone interviews documenting a large array of variables. Clinical information was abstracted from the medical files. The outcome measure was "return to work in good health" at 2 years, a variable that combined patients' occupational status, functional limitations and recurrences of work absence. Predictive models of 2-year outcome were developed with a recursive partitioning approach on a 40% random sample of our study subjects, then validated on the rest. RESULTS: The best predictive model included 7 baseline variables (patient's recovery expectations, radiating pain, previous back surgery, pain intensity, frequent change of position because of back pain, irritability and bad temper, and difficulty sleeping) and was particularly efficient at identifying patients with no adverse occupational outcome (negative predictive value 78%- 94%). INTERPRETATION: A clinical prediction rule accurately identified a large proportion of workers with back pain consulting in a primary care setting who were at a low risk of an adverse occupational outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".