External Validation of the Orebro Musculoskeletal Pain Screening Questionnaire within an Injured Worker Population: A Retrospective Cohort Study
Bibliographic record
Abstract
Purpose: The purpose of this study was to determine what cut-off of the Orebro Musculoskeletal Pain Screening Questionnaire score will best differentiate workers with acute musculoskeletal injuries at-risk for delayed return to work (greater than 3 months), in a population of workers of less than 3 weeks injury duration. Study Design: Retrospective cohort design, using a sample of convenience. Methods: A sample of 259 consecutive WCB patients seeking assessment and treatment at a multidisciplinary rehabilitation facility were reviewed, with 152 meeting the inclusion criteria of having sustained a soft tissue injury within 3 weeks of initial assessment. Descriptive statistics, tests of difference between Time 1 and Time 2 OMPSQ scores and Receiver Operator Characteristic curves were generated. The method of determining predictive ability of the OMPSQ at two points in time was by means of ROC analysis. Results: This study determined that the OMPSQ is moderately predictive of failure to achieve timely return to work (RTW) in a population of injured workers with acute musculoskeletal soft tissue injuries, when assessed two-weeks after treatment is initiated, and less predictive at the initial intake into treatment. Delayed RTW was defined as those workers who had not returned to their pre-injury job full time by 90 days, due to reduced functional ability as it related to their pre-injury occupation. Conclusions: This study demonstrates that there is variability in cut-off scores across studies. Future research should attempt to define cut-off scores as they relate to the population , outcome, condition and time-frame of interest .
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".