Microscopic Positive Tumor Margin Increases Risk for Disease Persistence but Not Recurrence in Patients with Stage T1-T2 Differentiated Thyroid Cancer
Bibliographic record
Abstract
Introduction. Differentiated thyroid cancer (DTC) has an overall excellent prognosis. Patients who develop recurrent disease have a more unfavorable disease course than those with no recurrence. Higher recurrence rates are seen with incomplete surgical resection and gross positive margins. It is unclear whether microscopic positive margin affects disease recurrence rates as much as grossly positive margin. Aim of the Study. To assess whether microscopic positive margin is an independent predictor of disease recurrence in patients with overall low-risk DTC. Patients and Methods. We conducted a retrospective single-center institutional review of 1,583 consecutive patients’ charts from 1995–2013 using the Canadian Thyroid Cancer Consortium Registry. We included adult patients with nonmetastasizing T1 and T2 DTC with a minimum of three years follow-up. Univariate and multivariate analyses were used to study factors that may influence the risk of persistent/recurrent disease. Strict definitions of persistent versus recurrent disease were applied. Results. 963 patients (152 men and 811 women) were included in the study with a mean age of 46 years. Microscopic positive margins were present in 12% of the specimens and were associated with an increased rate of persistent disease (8% versus 2% in the controls) but not with an increased risk of recurrent disease (1% in both groups). T2 tumors had a significantly higher incidence of positive margins than T1 tumors (48% versus 36%) and significantly higher nodal staging. Conclusions. Microscopic positive margin in the histopathology report in patients with low-risk DTC was associated with a higher rate of persistent disease but did not increase the risk of disease recurrence. A close follow-up of biomarkers and occult residual cancerous lesions is needed, especially in the first year. Further studies are needed to determine whether additional therapeutic measures to prevent recurrence are indicated in T1 and T2 DTC with positive microscopic surgical margins.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".