Plasma circulating tumor DNA as a potential tool for disease monitoring in head and neck cancer
Bibliographic record
Abstract
BACKGROUND: Recommendations for perioperative therapy in head and neck cancer are not explicit and recurrence occurs frequently. Circulating tumor DNA is an emerging cancer biomarker, but has not been extensively explored for detection of recurrence in head and neck cancer. METHODS: Patients diagnosed with head and neck squamous cell carcinoma were recruited into the study protocol. Tumors were sequenced to identify patient-specific mutations. Mutations were then identified in plasma circulating tumor DNA from pre-treatment blood samples and longitudinally during standard follow-up. Circulating tumor DNA status during follow-up was correlated to disease recurrence. RESULTS: Samples were taken from eight patients. Tumor mutations were verified in seven patients. Baseline circulating tumor DNA was positive in six patients. Recurrence occurred in four patients, two of whom had detectable circulating tumor DNA prior to recurrence. CONCLUSION: Circulating tumor DNA is a potential tool for disease and recurrence monitoring following curative therapy in head and neck cancer, allowing for better prognostication, and/or modification of treatment strategies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".