Ending 40 years of silence: Rationale for a new staging system for soft tissue sarcoma of the head and neck
Bibliographic record
Abstract
The tumor, node, metastases (TNM) anatomic staging system plays a pivotal role in cancer care, research, and cancer control activities. Since the first edition of the American Joint Committee on Cancer TNM staging classification published in 1977, soft tissue sarcomas have been staged in an anatomic site agnostic fashion whereby the primary tumor size (T) was categorized as T1 if <=5 cm and T2 if >5 cm; this remained unchanged through the 7th edition of the TNM. However, soft tissue sarcomas of the head and neck (STSHN) usually present smaller than sarcomas of other sites, but carry a disproportionate risk of local recurrence. Up to 70% of tumors are less than 5 cm at presentation, and therefore classified together as T1. Given the rarity of STSHN, there is a paucity of data to guide progress in their classification. Moreover, the majority of publications only report tumor size as less than or greater than 5 cm, presumably based on conventions of the TNM system that remained unchanged for 40 years, thereby affecting progress of STSHN classification. This formed the impetus for change in the 8th edition in 2 key ways: 1) several soft tissue sarcoma site based changes occurred including STSHN now having its own system, and 2) primary tumor size cut-offs of 2 cm and 4 cm used in STSHN now reflect sizes that head and neck specialists commonly encounter in their practice. This update was pragmatic in modifying the TNM from a system with a T category not serving STSHN and which was originally based on sarcoma data from non-head and neck anatomic sites. The background to this change is outlined which provides a framework in which data can be reported to generate evidence for future staging modifications.
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 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 | 0.056 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.015 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".