Bibliographic record
Abstract
Treatment outcome of 72 chronic back pain patients was assessed with 4 standardized measures: the McGill Pain Questionnaire, the Audiovisual Taxonomy of Pain Behavior, the Beck Depression Inventory, and the Profile of Mood States. Patients were also rated by their primary nurse on pain behavior, activity, drug-seeking, and sleep. Variables used to predict scores on outcome measures included patients' demographics and MMPI scores. Multiple regression analyses indicated that patients receiving worker's compensation engaged in more pain behavior and rated their pain as more severe, both upon admission and discharge from the pain program. High scores on the MMPI Hy scale were correlated with high self-ratings of pain and several MMPI scales correlated with negative mood. Even though demographic variables predicted admission and discharge scores on a number of treatment outcome measures, there was no relation between demographics and patients' improvement on treatment outcome measures. Patients with high scores on MMPI Hy and D scales displayed greatest admission to discharge improvement on self-rated pain and mood. Results were integrated with findings from previous studies. It was suggested that by distinguishing between overall scores on treatment outcome measures and improvement on these measures, professionals will be in a better position to devise individualized treatment plans for pain patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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 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".