Bibliographic record
Abstract
This review discusses the recent literature on pain conditions in children that should be of interest to rheumatologists. The focus of the review is therefore on musculoskeletal pains in children, particularly chronic or recurrent musculoskeletal pain. Articles that have a broader focus on pain are discussed when these are likely to be of general interest to rheumatologists. Chronic or recurrent pain in childhood is common and can be caused by a wide variety of conditions, several of which are discussed here. The importance of being able to measure pain in children has been emphasized repeatedly in the recent literature. With increased understanding of how to evaluate pain in children has come the recognition that pain in children is multifactorial and that even when there are obvious "organic" causes of the pain (such as arthritis), psychosocial factors are critical in how pain is perceived, and they influence the extent to which pain leads to dysfunction. There is also increasing evidence that cognitive-behavioral therapies are effective in managing chronic pain in children. The frequency of back pain in children is increasingly recognized, and the role of children's work and play, carrying heavy backpacks, and sitting for long periods of time at computers in causing back pain is of interest. The studies reviewed here add to an increasingly rich and informative literature on musculoskeletal and other chronic pain in children, and they help emphasize the importance of proper evaluation and management of pain in children.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.009 | 0.003 |
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".