Efficacy and Safety of Central Compartment Neck Dissection for Recurrent Thyroid Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To explore the safety and efficacy of central compartment neck dissection (CCND) in the treatment of well-differentiated thyroid carcinoma (WDTC) recurrences in the central compartment of the neck. DESIGN: Retrospective medical chart review. SETTING: Tertiary-care academic hospital. PATIENTS: Eighty-two consecutively treated patients with recurrent WDTC, with a median follow-up of 28 months. MAIN OUTCOME MEASURES: Postoperative complications, disease control posttreatment, and normalization of serum thyroglobulin (Tg) level. RESULTS: Eighty-two patients underwent 86 central compartment procedures. Only CCND was performed in 36 patients (42%), while a lateral neck dissection was also required in the remainder. Postoperative hypoparathyroidism was temporary in 17 patients (20%) and permanent in 6 patients (7%). Postoperative intact serum parathyroid hormone level was greater than 15.0 pg/mL (to convert to nanograms per liter, multiply by 1.0) in 81% of patients, accurately predicting eucalcemia postoperatively. Unilateral recurrent laryngeal nerve injury was transient in 3 patients (2% of nerves at risk) and permanent in 3 patients (2%). Seventeen patients (21%) experienced subsequent recurrences after their CCND-2 patients (2%) had recurrence in the central neck, 8 (9%) in the lateral neck, 2 (2%) in the central and lateral neck, and 7 (8%) at distant sites. Twenty-seven patients underwent a CCND alone and were deemed appropriate for efficacy analysis. The Tg level was normalized in 15 patients (56%) in the group overall. Normalization occurred in 10 of the 20 patients (50%) who received sodium iodide I 131 ablation and in 5 of the 7 patients (71%) who did not. CONCLUSION: Central compartment neck dissection is a safe and efficacious procedure for the management of central neck recurrences in WDTC.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".