1999 Young Investigator Research Award Winner
Bibliographic record
Abstract
STUDY DESIGN: Prospective inception cohort study. OBJECTIVE: To develop a prognostic model that predicts time receiving workers' compensation benefits for low back pain claimants. SUMMARY OF BACKGROUND DATA: As the cost and difficulty of managing low back pain escalate, any predictor of outcome is advantageous. METHODS: To obtain the outcome and predictor variables, patient data from two separate databases were linked: a clinical database and an administrative (Ontario workers' compensation) database. Claimants injured between January 1 and December 31, 1994, were included and observed for 1 year from the date of accident. The outcome variable was cumulative number of calendar days receiving benefits. RESULTS: Multivariable Cox proportional hazards regression (forward stepwise) showed eight significant predictors; five were associated with increased time receiving benefits compared with their reference groups: 1) working in the construction industry, 2) older age, 3) lag time from injury to treatment, 4) pain referred into the leg, and 5) three or more positive Waddell nonorganic signs. Three predictors were associated with reduced time receiving benefits: 1) higher values of questionnaire score, 2) intermittent pain, and 3) a previous episode of back pain. A predictive score was calculated to categorize claimants as at high or low risk for chronicity. When an arbitrary cutoff point was set at the 75th percentile of predictive score, negative predictive value was 94%. CONCLUSION: This research identified eight factors for time receiving workers' compensation benefits among claimants with low back pain. This model discriminates between high- and low-risk claimants. Few low-risk claimants continued to receive benefits for more than 3 months.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.142 | 0.077 |
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".