Identification of pain-related psychological risk factors for the development and maintenance of pediatric chronic postsurgical pain
Bibliographic record
Abstract
BACKGROUND: The goals of this study were to examine the trajectory of pediatric chronic postsurgical pain (CPSP) over the first year after surgery and to identify acute postsurgical predictors of CPSP. METHODS: Eighty-three children aged 8-18 years (mean 13.8, standard deviation 2.4) who underwent major orthopedic or general surgery completed pain and pain-related psychological measures at 48-72 hours, 2 weeks (pain anxiety and pain measures only), and 6 and 12 months after surgery. RESULTS: Results showed that 1 year after surgery, 22% of children developed moderate to severe CPSP with minimal functional disability. Children who reported a Numeric Rating Scale pain-intensity score ≥ 3 out of 10 two weeks after discharge were more than three times as likely to develop moderate/severe CPSP at 6 months and more than twice as likely to develop moderate/severe CPSP at 12 months than those who reported a Numeric Rating Scale pain score < 3 (6-month relative risk 3.3, 95% confidence interval 1.2-9.0 and 12-month relative risk 2.5, 95% confidence interval 0.9-7.5). Pain unpleasantness predicted the transition from acute to moderate/severe CPSP, whereas anxiety sensitivity predicted the maintenance of moderate/severe CPSP from 6 to 12 months after surgery. CONCLUSIONS: This study highlights the prevalence of pediatric CPSP and the role played by psychological variables in its development/maintenance. Risk factors that are associated with the development of CPSP are different from those that maintain it.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".