Systematic Review and Meta-Analysis of Randomized Controlled Trials for Scalp Block in Craniotomy
Bibliographic record
Abstract
BACKGROUND: Scalp block is regional anesthetic injection along nerves innervating the cranium. Scalp blocks for craniotomy may decrease postoperative pain and opioid consumption. Benefits may extend beyond the anesthetic period. OBJECTIVE: To analyze evidence for scalp block on postoperative pain and opioid use. METHODS: This systematic review and meta-analysis, Prospective Register of Systematic Reviews registration (CRD42022308048), included Ovid Medical Literature Analysis and Retrieval System Online, Embase, and Cochrane Central Register of Controlled Trials inception through February 9, 2022. Only randomized controlled trials were included. We excluded studies not reporting either main outcome. Duplicate reviewers performed study selection, risk of bias assessment, data extraction, and evidence certainty Grading of Recommendations Assessment, Development, and Evaluation appraisal. Main outcomes were postoperative pain by visual analog scale within 72 hours and opioid consumption as morphine milligram equivalent (MME) within 48 hours. RESULTS: Screening filtered 955 studies to 23 trials containing 1532 patients. Risk of bias was overall low. Scalp block reduced postoperative pain at 2 through 72 hours, visual analog scale mean differences of 0.79 to 1.40. Opioid requirements were reduced at 24 hours by 16.52 MME and 48 hours by 15.63 MME. CONCLUSION: Scalp block reduces postoperative pain at 2 through 48 hours and may reduce pain at 72 hours. Scalp block likely reduces opioid consumption within 24 hours and may reduce opioid consumption to 48 hours. The clinical utility of these differences should be interpreted within the context of modest absolute reductions, overall care optimization, and patient populations. This is the first level 1A evidence to evaluate scalp block efficacy in craniotomy.
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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 | 0.036 | 0.120 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.051 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".