Categorizing Patients With Occupational Low Back Pain by Use of the Quebec Task Force Classification System Versus Pain Pattern Classification Procedures: Discriminant and Predictive Validity
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Quebec Task Force Classification (QTFC) and pain pattern classification (PPC) procedures, including centralization and noncentralization, are common classification procedures. Classification was done to estimate validity of data obtained with QTFC and PPC procedures for differentiating patient subgroups at intake and for use in predicting rehabilitation outcomes at discharge and work status at 1 year after discharge from rehabilitation. SUBJECTS: Patients (n=171, 54% male; mean age=37 years, SD=10, range=18-62) with acute work-related low back pain referred for physical therapy were analyzed. METHODS: Patients completed pain and psychosocial questionnaires at initial examination and discharge and pain diagrams throughout intervention. Physical therapists classified patients using QTFC and PPC data at intake. Patients were classified again at discharge by PPC (time-dependent PPC). RESULTS: Analysis of variance of showed QTFC and PPC data could be used to differentiate patients by pain intensity or disability at intake. Analysis of covariance showed that intake PPC predicted pain intensity and disability at discharge, but QTFC did not. Logistic regression showed that PPC predicted work status at 1 year, but QTFC did not. Classifying patients over time using time-dependent PPC data reduced the false positive rate by 31% and increased percentage of change in pretest-posttest probability of return to work by 16% compared with classifying patients at intake. DISCUSSION AND CONCLUSION: Results support the discriminant validity of the QTFC data at intake and predictive validity of the PPC data at intake. Tracking PPC over time increases predictive validity for 1-year work status.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".