Risk factors for persistent pain after breast and thoracic surgeries: a systematic literature review and meta-analysis
Bibliographic record
Abstract
ABSTRACT: Persistent postsurgical pain (PPSP) is common after breast and thoracic surgeries. Understanding which risk factors consistently contribute to PPSP will allow clinicians to apply preventive strategies, as they emerge, to high-risk patients. The objective of this work was to systematically review and meta-analyze the literature on risk factors of PPSP after breast and thoracic surgeries. A systematic literature search using Ovid Medline, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, and Scopus databases was conducted. Study screening with inclusion and exclusion criteria, data extraction, and risk of bias assessment was performed independently by 2 authors. The data for each surgical group were analyzed separately and meta-analyzed where possible. The literature search yielded 5584 articles, and data from 126 breast surgery and 143 thoracic surgery articles were considered for meta-analysis. In breast surgery, younger age, higher body mass index, anxiety, depression, diabetes, smoking, preoperative pain, moderate to severe acute postoperative pain, reoperation, radiotherapy, and axillary lymph node dissection were the main factors associated with higher risk of PPSP. In thoracic surgery, younger age, female sex, hypertension, preoperative pain, moderate to severe acute postoperative pain, surgical approach, major procedure, and wound complications were associated with PPSP. This systematic review demonstrated certain consistent risk factors of PPSP after breast and thoracic surgeries, as well as identified research gaps. Understanding the factors that increase susceptibility to PPSP can help selectively allocate resources to optimize perioperative care in high-risk patients and help develop targeted, risk-stratified interventions for PPSP prevention.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".