Paravertebral block and persistent postoperative pain after breast surgery: meta‐analysis and trial sequential analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examined whether paravertebral block has an effect on the prevalence of persistent postsurgical pain after breast surgery. Seven randomised, controlled trials (559 patients) which had the outcome assessor blinded were included, comparing patients who received paravertebral blocks after breast surgery with patients who did not. The risk ratio (95% CI) was 0.75 (0.48-1.15) for the incidence of postoperative pain at 3 months (four studies, 317 patients); the risk ratio (95% CI) obtained from three studies including 301 patients reporting on pain after 6 months was 0.57 (0.29-1.72), and the risk ratio (95% CI) for pain after 12 months (three trials, 237 patients) was 0.42 (0.15-1.23). Conventional meta-analysis using the random effects model thus showed no statistically significant risk reduction for persistent postoperative pain at 3 months, 6 months or 12 months. Trial sequential analysis, used to consider the risk of type 1 and type 2 random error, showed that at 3 months, 6 months and 12 months, the number of subjects in the analyses were only 18.3%, 6.8% and 4.2% of the required information sizes at those time points, respectively. Our study is the first to evaluate data on pain 12 months postoperatively. Trial sequential analysis revealed that the current evidence is not sufficient to reach a conclusion. These findings stand in contrast to previous meta-analyses with fewer studies that had concluded that paravertebral block effectively reduces chronic pain.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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 it