The Fear Avoidance Model predicts short-term pain and disability following lumbar disc surgery
Bibliographic record
Abstract
OBJECTIVE: To examine the prognostic value of the Fear Avoidance Model (FAM) variables when predicting pain intensity and disability 10-weeks postoperative following lumbar disc surgery. METHODS: We recruited patients scheduled for first-time, single level lumbar disc surgery. The following aspects of the FAM were assessed at preoperative baseline and after 10 postoperative weeks: numeric pain rating scale (0-10) for leg and back pain intensity separately, Pain Catastrophizing Scale (PCS), Fear Avoidance Beliefs Questionnaire (FABQ), Beck Depression Inventory (BDI), Oswestry Disability Questionnaire (ODI), and the International Physical Activity Questionnaire (IPAQ). Multivariate regression models were used to examine the best combination of baseline FAM variables to predict the 10-week leg pain, back pain, and disability. All multivariate models were adjusted for age and sex. RESULTS: 60 patients (30 females, mean [SD] age = 40.4 [9.5]) were enrolled. All FAM measures correlated with disability at baseline. Adding FAM variables to each of the stepwise multiple linear regression model explained a significant amount of the variance in disability (Adj. R2 = .38, p < .001), leg pain intensity (Adj. R2 = .25, p = .001), and back pain intensity Adj. R2 = .32, p < .001 at 10-weeks). After adjusting for age and gender, BDI and FABQ-work subscale were the only significant predictors added to each of the prediction models for the 10-week clinical outcome (leg pain, back pain, and ODI). CONCLUSION: BDI and FABQ-work subscale variables are associated with baseline pain intensity and disability and predict short-term pain and disability following lumbar disc surgery. Measuring these variables in patients being considered for lumbar disc surgery may improve patient outcome.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".