Intercostobrachial nerve preservation during breast cancer surgery to prevent chronic postsurgical pain: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Intercostobrachial nerve (ICBN) sacrifice during breast cancer surgery is associated with chronic postsurgical pain (CPSP), impaired sensory function, and reduced quality of life. The aim of this study was to assess the comparative effects of ICBN preservation vs. sacrifice after breast cancer surgery. METHODS: We searched six databases for randomized clinical trials (RCTs) exploring ICBN preservation vs. sacrifice during breast cancer surgery. We assessed risk of bias and conducted random-effects meta-analyses, using the GRADE approach to rate the certainty of evidence. RESULT: We included 25 RCTs and 2560 patients. Moderate certainty evidence showed that, compared to nerve sacrifice, ICBN preservation probably reduces CPSP (relative risk [RR] 0.27, 95 % confidence interval [CI] 0.21 to 0.34; absolute risk reduction [ARR] 18 % [95 %CI 16 %-20 %]) and sensory deficits (RR 0.25 [0.18 to 0.35]; ARR 28 % [24 %-30 %]). Low certainty evidence suggested that ICBN preservation may reduce moderate-to-severe CPSP (RR 0.26 [0.10 to 0.65]; ARR 7 % [4 %-9 %]), hypoesthesia (RR 0.40 [0.25 to 0.64]; ARR 17 % [10 %-21 %]), and paraesthesia (RR 0.30 [0.13 to 0.69]; ARR 10 % [4 %-12 %]), but prolong surgery by an average of 11 min [95 %CI 4-18 min]. The effect on breast cancer recurrence (RR 0.83 [0.51 to 1.35]) was supported by only very low certainty evidence. CONCLUSIONS: Compared with ICBN sacrifice, preservation of the ICBN during breast cancer surgery probably reduces CPSP and sensory deficits but may increase the duration of surgery. REGISTRATION: Open Science Framework (https://osf.io/r3knf).
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".