Perineural invasion and spread in head and neck cancer
Bibliographic record
Abstract
Perineural involvement is a well-recognized clinicopathologic entity found in head and neck (H&N) cancers, including mucosal epithelial carcinomas and salivary gland malignancies. Perineural disease remains a diagnostic, prognostic and therapeutic challenge for the multidisciplinary H&N oncology team. Nerves are important routes of tumor spread in H&N malignancies, yet the biology and prognostic implications of perineural tumor growth are not fully understood. On balance, the available evidence suggests that it is associated with an increased risk of locoregional recurrence but the impact on survival remains uncertain. Perineural involvement has implications for locoregional disease diagnosis and management. MRI is the best imaging modality to detect tumor extent. Advanced radiotherapy technologies such as intensity-modulated radiation therapy and image-guided radiation therapy have the potential for more accurate targeting and treatment of anatomically complex patterns of disease spread. This review is limited to nondermatologic H&N 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".