A Systematic Review and Metaanalysis of the Effectiveness of Radioactive Iodine Remnant Ablation for Well-Differentiated Thyroid Cancer
Bibliographic record
Abstract
Radioactive iodine remnant ablation destroys residual thyroid tissue after surgical resection of papillary or follicular thyroid cancer. We systematically reviewed 1543 English references to determine whether remnant ablation decreases the risk of thyroid cancer-related death or recurrence after bilateral thyroidectomy for papillary or follicular thyroid cancer. In 13 cohort studies in which the analysis of thyroid cancer-related outcomes was statistically adjusted to a variable degree for prognostic factors or cointerventions, rates of recurrences of thyroid cancer-related outcomes were significantly decreased in the following: one of seven studies examining thyroid cancer-related mortality, three of six studies examining any tumor recurrence, three of three studies examining locoregional recurrence, and two of three studies examining distant metastases. Thyroid hormone suppressive therapy was not adjusted for in the majority of these analyses. In 18 cohort studies not adjusted for prognostic factors or interventions, the benefit of radioactive iodine ablation in decreasing the thyroid cancer-related mortality and any recurrence at 10 yr was inconsistent among centers. However, pooled analyses were suggestive of a statistically significant treatment effect of ablation for the following 10-yr outcomes: locoregional recurrence (relative risk of 0.31, 95% confidence interval, 0.2, 0.49) and distant metastases (absolute decrease in risk 3%, 95% confidence interval, risk decreases 1-4%). In conclusion, radioactive iodine ablation may be beneficial in decreasing recurrence of well-differentiated thyroid cancer; however, results are inconsistent among centers for some outcomes, and the incremental benefit of remnant ablation in low-risk patients treated with bilateral thyroidectomy and thyroid hormone suppressive therapy is unclear.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".