Sentinel lymph node biopsy in squamous cell carcinoma of the head and neck: where we stand now, and where we are going.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This review was performed to evaluate the existing literature on sentinel lymph node biopsy (SLNB) for early-stage oral and oropharyngeal head and neck squamous cell carcinoma (HNSCC) in clinically negative (N0) necks. METHODS: A Medline search identified 43 pertinent published trials and reviews in the English-language literature from 1990 to 2005. RESULTS: Recent studies consistently show high sensitivities > 93% for T1 and T2 HNSCC. SLNB has the potential to replace neck dissection in those patients. Data on T3 and T4 tumours are not as promising, although research is currently under way to determine the true metastasis detection rate. Appropriate technique is crucial for the complete detection of the sentinel nodes. For HNSCC sentinel lymphadenectomy, many studies have advocated the use of a colloid tracer and gamma probe detector, as well as the harvesting of a total of three nodes as a good standard technique. CONCLUSIONS: American multicentre trials are currently under way gathering crucial data on this technique. It is very likely that SLNB will become indicated for T1 and T2 oral cavity squamous cell carcinoma with N0 necks, and it is possible that the indication will extend to all early-stage HNSCCs. However, more research will be necessary for advanced head and neck cancers.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".