Is it time to reconsider lobectomy in low‐risk paediatric thyroid cancer?
Bibliographic record
Abstract
OBJECTIVE: Current guidelines recommend total thyroidectomy for nearly all children with well-differentiated thyroid cancer (WDTC). These guidelines, however, derive from older data accrued prior to current high-resolution imaging. We speculate that there is a subpopulation of children who may be adequately treated with lobectomy. DESIGN: Retrospective analysis of prospectively maintained database. PATIENTS: Seventy-three children with WDTC treated between 2004 and 2015. MEASUREMENTS: We applied two different risk-stratification criteria to this population. First, we determined the number of patients meeting American Thyroid Association (ATA) 'low-risk' criteria, defined as disease grossly confined to the thyroid with either N0/Nx or incidental microscopic N1a disease. Second, we defined a set of 'very-low-risk' histopathological criteria, comprising unifocal tumours ≤4 cm without predefined high-risk factors, and determined the proportion of patients that met these criteria. RESULTS: Twenty-seven (37%) males and 46 (63%) females were included in this study, with a mean age of 13·4 years. Ipsilateral- and contralateral multifocality were identified in 27 (37·0%) and 19 (26·0%) of specimens. Thirty-seven (51%) patients had lymph node metastasis (N1a = 18/N1b = 19). Pre-operative ultrasound identified all cases with clinically significant nodal disease. Of the 73 patients, 39 (53·4%) met ATA low-risk criteria and 16 (21·9%) met 'very-low-risk' criteria. All 'very-low-risk' patients demonstrated excellent response to initial therapy without persistence/recurrence after a mean follow-up of 36·4 months. CONCLUSIONS: Ultrasound and histopathology identify a substantial population that may be candidates for lobectomy, avoiding the risks and potential medical and psychosocial morbidity associated with total thyroidectomy. We propose a clinical framework to stimulate discussion of lobectomy as an option for low-risk patients.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".