Should thoracic paravertebral blocks be used to prevent chronic postsurgical pain after breast cancer surgery? A systematic analysis of evidence in light of IMMPACT recommendations
Bibliographic record
Abstract
The role of thoracic paravertebral block (PVB) in preventing chronic postsurgical pain (CPSP) after breast cancer surgery (BCS) has gained interest, but existing evidence is conflicting, and its methodological quality is unclear. This meta-analysis evaluates efficacy of PVB, compared with Control group, in preventing CPSP after BCS, in light of the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) recommendations. Electronic databases were searched for randomized trials comparing PVB with Control group for CPSP prevention after BCS. Eligible trials were assessed for adherence to IMMPACT recommendations. The primary outcomes were CPSP at 3 and 6 months, whereas secondary outcomes were PVB-related complications. Data were pooled and analyzed using random-effects modelling. Trial sequential analysis was used to evaluate evidence conclusiveness. Data from 9 studies (604 patients) were analyzed. The median (range) of IMMPACT recommendations met in these trials was 9 (5, 15) of 21. Paravertebral block was not different from Control group in preventing CPSP at 3 months, but was protective at 6 months, with relative risk reduction (95% confidence interval) of 54% (0.24-0.88) (P = 0.02). Meta-regression suggested that the relative risk of CPSP was lower when single-injection (R = 1.00, P < 0.001) and multilevel (R = 0.71, P = 0.01) PVB were used. Trial sequential analysis revealed that 6-month analysis was underpowered by at least 312 patients. Evidence quality was moderate according to the GRADE system. Evidence suggests that multilevel single-injection PVB may be protective against CPSP at 6 months after BCS, but methodological limitations are present. Larger trials observing IMMPACT recommendations are needed to confirm this treatment effect and its magnitude.
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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 | 0.043 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 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".