Risk and protective factors in predicting pediatric acute postsurgical pain: A systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Acute postsurgical pain (APSP), defined as pain within 3 months after surgery, is reported in most surgical pediatric patients, and a significant number of patients experience pain interfering with their daily life activities. We aimed to identify perioperative and psychosocial factors associated with APSP severity in pediatric patients undergoing surgery. METHOD: MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, and CENTRAL were searched from database inception to October 2021. Studies that reported an association between risk or protective factors and acute pain in children were included. The primary outcome was the magnitude of association between identified factors and APSP, as measured by standardized effect sizes. RESULTS: Thirty-eight studies (7,936 participants aged 1-18 years) were included. Meta-analysis of 12 studies (1,192 participants) revealed child preoperative pain, pain immediately after surgery, anticipated pain, temperament, pain catastrophizing, age, preoperative anxiety, parent pain catastrophizing, and parent preoperative anxiety were positively associated with APSP. Child pain coping efficacy was protective against APSP. We identified several modifiable child and parent psychosocial factors as predictors of APSP severity. CONCLUSION: Given the small degree of association between identified factors and postsurgical pain, there is value in pursuing other factors that may better explain the variability in pain. Recognizing patients at risk for moderate to severe APSP enables early implementation of interventions to minimize pain burden. Interventions to enhance coping, an adaptive characteristic, may also help to reduce APSP. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".