Bibliographic record
Abstract
The purpose of this review is to provide a clearer picture of the present status of self-report assessment tools for comorbid chronic pain and depression. The utility of several independent self-report measures of chronic pain and depression found in the literature will be evaluated. The scope of the review will be narrowed to include only the most commonly used measures and a brief history of pain assessment. The instruments reviewed are the Minnesota Multiphasic Personality Inventory-2 (MMPI-2), the Beck Depression Inventory (BDl), the Symptom Checklist-90R (SCL-90R), pain drawings, Verbal Rating Scales (VRS), Numerical Rating Scales (NRS), Visual Analog Scales (VAS), Descriptor Differential Scales (DDS), the McGill Pain Questionnaire (MPQ), and the Medical Outcomes Study short-form general health survey-36 (SF-36). The conclusion drawn from this review was that while there are several independent measures of chronic pain and depression that are relatively valid and reliable, there is no one measure or cluster of measures that clearly elucidates the severity and impact of comorbid chronic pain and depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".