Targeting Molecular Residual Disease Using Novel Technologies and Clinical Trials Design in Head and Neck Squamous Cell Cancer
Bibliographic record
Abstract
Abstract High-risk human papillomavirus (HPV)-related and most cases of HPV-negative locoregionally advanced head and neck squamous cell carcinoma (LA-HNSCC) have substantial risks of relapse despite definitive therapy, and thus represent conditions of unmet clinical need. The ability now exists to detect molecular residual disease (MRD) in these patients post-definitive treatment such as surgery or (chemo)radiotherapy using novel and highly sensitive and specific technologies to measure cancer-derived circulating biomarkers. The positive and negative predictive values of these assays to forecast cancer recurrence, as well as the lead time of circulating tumor DNA (ctDNA) detection before clinical relapse, are relevant as these parameters rationalize the design of clinical trials for cancer interception in the MRD setting. Currently, there is evidence that interception in the MRD setting yields benefit in clinical outcome in some cancers, but such data do not yet exist in LA-HNSCC and will require prospective testing via clinical trials.
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".