Bibliographic record
Abstract
OBJECTIVE: Do physical findings that are used to indicate location and extent of tissue damage and a measure of the severity of initial pain predict subsequent reports of pain and of disability? METHODOLOGY: A standardized literature search identified one systematic review and 12 observational studies (9 low back pain, 2 neck pain, and 1 carpal tunnel syndrome) to provide evidence about these questions. RESULTS: Most studies were of specific populations. These studies were useful studies of predictors, but they have limited generalizability. Exclusions and loss of subjects at follow-up in some studies also limited generalizability. Conclusions were made cautiously, because some factors with statistical correlations with chronic pain were not plausible predictors. CONCLUSIONS: The studies provide moderate evidence (level 2) that reports of the intensity of pain in acute musculoskeletal injury predict subsequent reports of pain. There is limited evidence (level 3) that the location and extent of injury predict reports of pain and poor functional activity outcomes. There is moderate evidence (level 2) that physical symptoms and signs cannot be considered individual predictors of chronic pain disability as measured by participation outcomes. Instead, in the transition from subacute to chronic pain disability, functional disability and psychological distress play a more important role than pain intensity.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".