Risk factors for neck hematoma requiring surgical re-intervention after thyroidectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: In this systematic review and meta-analysis, we aimed to determine the risk factors associated with neck hematoma requiring surgical re-intervention after thyroidectomy. METHODS: We systematically searched all articles available in the literature published in PubMed and CNKI databases through May 30, 2017. The quality of these articles was assessed using the Newcastle-Ottawa Quality Assessment Scale, and data were extracted for classification and analysis by focusing on articles related with neck hematoma requiring surgical re-intervention after thyroidectomy. Our meta-analysis was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. RESULTS: Of the 1028 screened articles, 26 met the inclusion criteria and were finally analyzed. The factors associated with a high risk of neck hematoma requiring surgical re-intervention after thyroidectomy included male gender (odds ratio [OR]: 1.86, 95% confidence interval [CI]: 1.60-2.17, P < 0.00001), age (MD: 4.92, 95% CI: 4.28-5.56, P < 0.00001), Graves disease (OR: 1.81, 95% CI: 1.60-2.05, P < 0.00001), hypertension (OR: 2.27, 95% CI: 1.43-3.60, P = 0.0005), antithrombotic drug use (OR: 1.92, 95% CI: 1.51-2.44, P < 0.00001), thyroid procedure in low-volume hospitals (OR: 1.32, 95% CI: 1.12-1.57, P = 0.001), prior thyroid surgery (OR: 1.93, 95% CI: 1.11-3.37, P = 0.02), bilateral thyroidectomy (OR: 1.19, 95% CI: 1.09-1.30, P < 0.0001), and neck dissection (OR: 1.55, 95% CI: 1.23-1.94, P = 0.0002). Smoking status (OR: 1.19, 95% CI: 0.99-1.42, P = 0.06), malignant tumors (OR: 1.00, 95% CI: 0.83-1.20, P = 0.97), and drainage used (OR: 2.02, 95% CI: 0.69-5.89, P = 0.20) were not significantly associated with postoperative neck hematoma. CONCLUSION: We identified certain risk factors for neck hematoma requiring surgical re-intervention after thyroidectomy, including male gender, age, Graves disease, hypertension, antithrombotic agent use, history of thyroid procedures in low-volume hospitals, previous thyroid surgery, bilateral thyroidectomy, and neck dissection. Appropriate intervention measures based on these risk factors may reduce the incidence of postoperative hematoma and yield greater benefits for the patients.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".