Radiofrequency ablation for treatment of benign thyroid nodules
Bibliographic record
Abstract
BACKGROUND: Thyroid nodules (TNs) usually appearing in the general population have the potential possibility of malignant transformation and common problems of jugular oppression such as dyspnea and hoarseness. We performed this meta-analysis to evaluate the efficiency of radiofrequency ablation (RFA) for the treatment of benign TNs in accord with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statements. METHODS: Published literatures were retrieved from PubMed, Embase, Web of Science, and Scopus up to January 27, 2016. Pooled standard mean difference with 95% confidence interval was estimated by fixed- or random-effects model depending on heterogeneity, which was calculated using the Cochran Q, τ, and I statistics. The quality of the articles was evaluated by the Newcastle-Ottawa scale. RESULTS: Meta-analysis of data from 1090 patients with 1406 benign TNs in 20 articles showed that with the subgroup stratified by nodule volume, they were significantly decreased at 1, 3, 6, 12, and the last follow-up months, when comparing post-RFA with the initial nodule volume. In addition, the volume also notably declined by cold and hot nodules. By subgrouping into the largest diameter, symptom score, cosmetic score, thyrotropin, triiodothyronine, free thyroxine level, and vascularity, the pooled data indicated that there was a decrease in largest diameter, symptom score, cosmetic score, triiodothyronine level, and vascular scale, an unchanged free thyroxine, and an increased thyrotropin level after RFA. The publication bias for this particular study is presented in the following groups: nodule volume reduction at 6 months and last follow-up month after RFA and symptom score. CONCLUSION: In summary, by pooling of these studies we recommended that RFA indeed has the advantages in improving outcomes and providing better prognosis for patients with benign TNs.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".