Bibliographic record
Abstract
Cancers of the head and neck have always represented a unique perspective in cancer staging. Not only are these lesions numerous in terms of anatomic sites of origin, but, unlike most other major cancers, they frequently and readily lend themselves to adequate clinical assessment by visual inspection and palpation, which greatly facilitates documentation by the trained clinician. In addition, their location often involves treatment programs that focus on nonsurgical organ-preservation strategies, and thus anatomic and histological data for comprehensive pathologic staging are often not available. Nevertheless, the processes involved in surgical decision-making and radiotherapy treatment planning require meticulous assessment and documentation of the extent of locoregional disease. For all these reasons it is especially important to perform reliable and accurate pretreatment clinical staging of head and neck cancers. Also, many patients who succumb to head and neck cancer do so as a result of locoregional disease. Therefore, the staging system must take into account detailed local anatomic features that dictate management, since the degree of involvement of these structures by tumor may be as important as distant metastasis in threatening survival. For this reason the most recent cancer staging classification (6th edition) of the International Union Against Cancer (UICC) and the American Joint Committee on Cancer (AJCC) includes new criteria for the more advanced cases (e.g., T4 categories and stage IV disease). These criteria reflect the fact that in heterogeneous populations there is a realistic opportunity for cure in some patients but not in others. This review summarizes the criteria used in the new TNM for head and neck tumors, and outlines the rationale behind the current changes. It also provides some guidance regarding optimal source data to facilitate classification in the registry setting. In addition, the need for additional changes in the future is recognized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".