Prophylactic Central Neck Dissection for Clinically Node‐Negative Papillary Thyroid Carcinoma
Bibliographic record
Abstract
OBJECTIVE: We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) that scrutinized the oncological benefits and postsurgical complications of total thyroidectomy (TT) plus prophylactic central neck dissection (pCND) versus TT alone among clinically node-negative (cN0) papillary thyroid cancer (PTC) patients. METHODS: We screened five databases from inception to September 4, 2021 and evaluated the risk of bias of the eligible studies. We pooled dichotomous outcomes using the risk ratio (RR) with 95% confidence interval (CI). RESULTS: Overall, we included 5 RCTs with low risk of bias comprising 795 patients (TT plus pCND = 410 and TT alone = 385). With regard to efficacy endpoint, the rate of structural loco-regional recurrence did not significantly differ between both groups (n = 4 RCTs, RR = 0.49, 95% CI [0.19, 1.27], P = .14). With regard to safety endpoints, the rates of hypoparathyroidism (n = 5 RCTs, RR = 1.48, 95% CI [0.73, 2.97], P = .27), recurrent laryngeal nerve injury (n = 5 RCTs, RR = 1.34, 95% CI [0.59, 3.03], P = .48), and bleeding (n = 3 RCTs, RR = 1.75, 95% CI [0.42, 7.26], P = .44) did not significantly differ between both groups. CONCLUSION: For cN0 PTC patients, there was no significant difference between TT plus pCND and TT alone with regard to the rate of structural loco-regional recurrence or frequency of postsurgical complications. Adaptation of pCND in cN0 PTC patients should be contemplated by taking into consideration the clinical oncological benefits and rate of postsurgical adverse events. LEVEL OF EVIDENCE: 1 Laryngoscope, 132:1320-1328, 2022.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".