Data Set for the Reporting of Nodal Excisions and Neck Dissection Specimens for Head and Neck Tumors: Explanations and Recommendations of the Guidelines From the International Collaboration on Cancer Reporting
Bibliographic record
Abstract
Standardized, synoptic pathologic reporting for tumors greatly improves communication among clinicians, patients, and researchers, supporting prognostication and comparison about patient outcomes across institutions and countries. The International Collaboration on Cancer Reporting is a nonprofit organization whose mission is to develop evidence-based, universally available surgical pathology reporting data sets. Within the head and neck region, lymph node excisions and neck dissections are frequently performed as part of the management of head and neck cancers arising from the mucosal sites (sinonasal tract, nasopharynx, oropharynx, hypopharynx, oral cavity, and larynx) along with bone tumors, skin cancers, melanomas, and other tumor categories. The type of specimen, exact location (lymph node level), laterality, and orientation (by suture or diagram) are essential to accurate classification. There are significant staging differences for each anatomic site within the head and neck when lymph node sampling is considered, most importantly related to human papillomavirus-associated oropharyngeal carcinomas and mucosal melanomas. Number, size, and site of affected lymph nodes, including guidelines on determining the size of tumor deposits and the presence of extranodal extension and soft tissue metastasis, are presented in the context of prognostication. This review elaborates on each of the elements included in the data set for Nodal Excisions and Neck Dissection Specimens for Head & Neck Tumours.
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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 | 0.160 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".