Is Suction Drainage an Effective Means ofPreventing Hematoma in Thyroid Surgery? A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy of suction drainage in preventing postoperative hematoma formation in thyroid surgery. METHODS: We conducted a meta-analysis using only randomized controlled trials in which the incidence of post-thyroidectomy hematoma was compared directly in patients with and without suction drains (eight studies since 1980; N = 944). The odds ratio (OR) with respective confidence intervals (CIs) using the fixed effects model was reported. We used an OR < 1.0 as being in favour of treatment (ie, the use of suction drains). RESULTS: In our meta-analysis, there was no statistically significant difference between the rates of post-thyroidectomy hematoma whether or not suction drains were used when the results were combined using a fixed effects model (OR 1.04, 95% CI-1.93), with p = .90. In this comparison, a fixed effects model was used rather than a random effects model because there was no statistically significant heterogeneity (chi2 = 6.26, p = .28). CONCLUSIONS: We conclude that the use of suction drains in thyroid surgery to prevent postoperative hematoma is not evidence based.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".