Meta-analysis: prophylactic drainage and bleeding complications in thyroid surgery.
Bibliographic record
Abstract
OBJECTIVE: To conduct a comprehensive systematic review and high-quality meta-analysis to determine whether prophylactic drain placement reduces adverse bleeding events in thyroid surgery. DATA SOURCES: MEDLINE (OVID and PubMed), CENTRAL, CDSR, ACP Journal Club, DARE, EMBASE, PREMEDLINE, OLDMEDLINE, CINAHL, BIOSIS Previews, LILACS, KOREAMED, SAMED, IndMED, SIGLE, ScienceDirect, and INGENTACONNECT. REVIEW METHODS: Studies for evaluation included all prospective trials assessing the use of drainage in thyroid surgery. We excluded case studies, retrospective studies, reviews, and studies that had a "selective" method of postoperative drainage that was not defined or was based on surgeon preference. Search strategies were broad and based on Cochrane Collaboration search filters. There was no language restriction. Article selection was conducted by two independent reviewers under QUORUM guidelines. RESULTS: Four hundred sixty-two articles were identified by the search strategy used, and 16 articles were included in the final review. Ten studies were randomized controlled trials, with 8 used for quantitative meta-analysis. No study showed a statistically significant benefit or harm with drain use. Meta-analysis of data estimated an odds ratio of 1.47 for reoperation for bleeding and 0.88 for visible hematoma for suction drains versus no drains. The results were not statistically significant, and 95% confidence intervals were wide. CONCLUSION: The literature has insufficient evidence to recommend routine drainage in thyroid surgery. It is possible that drains may increase the risk of reoperation for bleeding, although the data are not statistically significant. If there is a benefit to drainage, absolute risk reductions of bleeding outcomes may not warrant routine use.
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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 | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.055 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".