FC16 A prospective randomised trial of sentinel node biopsy for high risk non-melanoma skin cancer (SNIC TRIAL)
Bibliographic record
Abstract
Background Non-melanoma skin cancers are the most common skin cancers in the world. The majority do not metastasize, but those that do cause significant morbidity and mortality. Although it is not exactly known which tumours metastasize, certain risk factors have been recognized. These will be used to identify those at highest risk of lymph node metastases. By using the Sentinel Node Biopsy (SNB) we can identify and treat those that have metastasized at an early stage. Hypothesis Identification of early metastases and their treatment might improve survival and surgical morbidity of patients compared to more extensive surgery for delayed, more advanced lymph node disease. AimsPrimary aim To analyze disease free survival for patients with high risk non-melanoma skin cancer who undergo wide excision and sentinel node biopsy and immediate completion lymph node clearance in case of a positive sentinel node versus patients who undergo wide excision of the primary lesion with postoperative serial ultrasound observation of the regional lymphatic basin. Secondary aims To analyze overall and disease specific survival for both groups as well as for node positive patients per group and to monitor the morbidity of SNB and early versus delayed lymphadenectomy in high risk non-melanoma skin cancer. To detect genetic profiles corresponding to metastatic patterns in the primary tumour. Design The trial is a phase III, international, multicentre, randomized trial. In arm 1 of the study the treatment will be wide local excision of the skin tumour and sentinel node biopsy, followed by immediate lymphadenectomy in case of metastases in the sentinel node. In arm 2 of the study the treatment consists of wide local excision and nodal observation with ultra-sound follow up for 5 years. Lymph node metastases in the follow up will be treated by delayed lymphadenectomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".