Lymphatic mapping in second primary or recurrent oral cavity cancer with prior neck treatment: A case series and scoping review
Bibliographic record
Abstract
OBJECTIVES: Lymphatic mapping is an established technique to map drainage patterns in oral cancer. Its utility in patients who have undergone prior radiation or neck dissection is not well studied. METHODS: Patients presenting to a single tertiary cancer center between 2021-2023 for a recurrent/second oral cancer that underwent lymphatic mapping were considered. All patients had a history of a head and neck cancer treated with either radiation or neck dissection. We further conducted a scoping review in MEDLINE, Embase, and Web of Science of lymphatic mapping in oral cancer patients with previous neck treatment. RESULTS: In our single center review, a total of 11 patients were included. 73 % received prior radiotherapy and 55 % underwent prior neck dissections for a head and neck cancer. Lymphoscintigraphy-directed neck dissections identified sentinel nodes in 9/11 patients, with only one patient who had positive sentinel node disease. There were no reports of regional recurrence at a median of 10 months follow-up. Our scoping review of 980 studies identified 151 additional patients who underwent sentinel node biopsy for a second oral cancer after previous neck treatment. Overall, the negative predictive value of lymphatic mapping in all studies was 96.7 %. CONCLUSION: Lymphatic mapping is feasible in secondary or recurrent oral cavity cancers even in patients with prior radiation or surgical management of the neck. The literature to date demonstrates a negative predictive value of ∼ 97 % for sentinel node mapping and warrants further consideration in the management of salvage oral cancer.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".