Comparison of learning curves and related postoperative indicators between endoscopic and robotic thyroidectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Endoscopic thyroidectomy (ET) and robotic thyroidectomy (RT) yield similar perioperative outcomes. This study investigated how the learning curve (LC) affects perioperative outcomes between ET and RT, identifying factors that influence the LC. MATERIALS AND METHODS: Two researchers individually searched PubMed, EMBASE, Web of Science, and Cochrane Library for relevant studies published until February 2024. The Newcastle-Ottawa Scale assessed study quality. A random-effects model was used to compute the odds ratio and weighted mean difference (WMD). Poisson regression comparison of the number of surgeries (N LC ) was required for ET and RT to reach the stable stage of the LC. Heterogeneity was measured using Cochran's Q. Publication bias was tested using funnel plots, and sensitivity analysis assessed findings robustness. Subgroup analysis was done by operation type and patient characteristics. RESULTS: This meta-analysis involved 33 studies. The drainage volume of ET was higher than that of RT (WMD=-17.56 [30.22, -4.49]). After reaching the N LC , the operation time of ET and RT was shortened (ET: WMD=28.15 [18.04-38.26]; RT: WMD=38.53 [29.20-47.86]). Other perioperative outcomes also improved to varying degrees. Notably, RT showed more refined central lymph node resection (5.67 vs. 4.71), less intraoperative bleeding (16.56 ml vs. 42.30 ml), and incidence of transient recurrent laryngeal nerve injury (24.59 vs. 26.77). The N LC of RT was smaller than that of ET (incidence-rate ratios [IRR]=0.64 [0.57-0.72]). CUSUM analysis (ET: IRR=0.84 [0.72-0.99]; RT: IRR=0.55 [0.44-0.69]) or a smaller number of respondents (ET: IRR=0.26 [0.15-0.46]; RT: IRR=0.51 [0.41-0.63]) was associated with smaller N LC . In RT, transoral approach (IRR=2.73 [1.96-4.50]; IRR=2.48 [1.61-3.84]) and retroauricular approach (RAA) (IRR=2.13 [1.26-3.60]; IRR=1.78 [1.04-3.05]) had smaller N LC compared to bilateral axillo-breast and transaxillary approach (TAA). In ET, the N LC of RAA was smaller than that of TAA (IRR=1.61 [1.04-2.51]), breast approach (IRR=1.67 [1.06-2.64]), and subclavian approach (IRR=1.80 [1.03-3.14]). CONCLUSIONS: Rich surgical experience can improve surgical results of ET and RT. After reaching the N LC , the perioperative outcomes of RT are better than those of ET. Study subjects, surgical approaches, and analysis methods can affect N LC .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".