Efficacy and safety of thermal ablation modalities for the treatment of papillary thyroid microcarcinoma: Systematic Review and network meta-analysis
Bibliographic record
Abstract
Background: Thermal ablation (TA) modalities such as radiofrequency ablation (RFA), microwave ablation (MWA), and laser ablation (LA) have been widely used in the treatment of papillary thyroid microcarcinoma (PTMC) patients. Based on previous small-sample meta-analyses, this network meta was designed to further compare the efficacy and safety of these thermal ablation methods in PTMC patients. Methods: China National Knowledge Infrastructure (CNKI), Wanfang, PubMed, Embase, and the Cochrane Library databases were searched to retrieve relevant studies published before May 2022. The efficacy outcomes was recurrence and lymph node metastasis (LNM), the safety outcome included operation time, intra-operative blood loss, hospital stays and complications. The Newcastle-Ottawa Scale (NOS) was selected for the risk of bias assessment. Stata 14.0 was used for statistical analysis. Results: Twenty-nine articles were included. Based on as least 6 months follow up, our analysis discovered no significant statistical differences in all efficacy and safety outcomes between MWA, RFA, and LA groups. Moreover, three TA treatments all produced significant least operation time, hospital stays, and complications than surgery group. Additionally, our study found that RFA showed relatively less complications, LNM, operation time and intra-operative blood loss, compared with other TA treatments. MWA indicated the lowest probability of recurrence, LA showed the longest hospital stays. Conclusions: MWA, RFA, and LA are all effective treatment strategies for patients with PTMC, but could not completely replace conventional surgical treatment.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".