The Role of Surgery in Small Differentiated Thyroid Cancer
Bibliographic record
Abstract
Abstract IntroductionThe incidence of small, differentiated thyroid cancer (DTC) cases has been increasing in the United States and the world mainly due to incidental detection because of widespread use of diagnostic modalities. While the option of active surveillance instead of surgical resection is getting more popular, there is still an open discussion about the best approach in these cases.Materials and MethodsThe National Cancer Database was queried for patients diagnosed with non-metastatic small T1/N0 DTC between 2004 and 2016, who have known surgical status and Charlson comorbidity index of two or less. We evaluated the overall survival (OS) based on the surgery status using Kaplan-Meier estimates and multivariable cox regression analyses.ResultsA total of 98,501 patients with non-metastatic small DTC were included, within which 96,612 (98.1%) were treated with surgery, and 1,889 (1.9%) were not treated with surgery or other ablative modalities. We found that patients who were treated with surgery had better OS compared to patients who were not treated with surgery (mean OS 171 months vs 134.1 months, P<0.001, median OS was not reached). This difference was still statistically significant even after we used propensity score matching for age, gender, race, Charlson-Deyo score, tumor size, and histology. On multivariate analysis, surgery was associated with better OS (HR 0.218; 95% CI: 0.196 - 0.244; P<0.001).Same trend was found in subgroup analysis when we split the cohort according to tumor size (<1cm and ≥1cm), histology (follicular, papillary and Hurthle cell carcinoma), and age (< 55 years vs > 55 years).ConclusionPatients with non-metastatic small DTC who were treated with surgery had significant improvement in OS compared to patients who were not treated with surgery. Notwithstanding the limitations of the current analysis, these results call for caution prior to recommending routine surveillance for all patients with small DTC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".