Recurrent nasopharyngeal carcinoma: treatment outcomes and morbidity
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review highlights current management strategies for recurrent nasopharyngeal carcinoma (NPC) and their balance between efficacy and toxicity. RECENT FINDINGS: Optimal management of recurrent NPC requires individualized, multidisciplinary decisions that consider not only oncologic control but also functional outcomes and toxicity. Advances in imaging and Epstein--Barr virus DNA monitoring are improving early detection and risk stratification, helping tailor salvage approach to patient and disease factors. Recent evidences also shows that severe toxicity is not only modality-dependent but is shaped by patient and disease-related factors. SUMMARY: Endoscopic nasopharyngectomy achieves the best outcomes in resectable cases, with higher survival and lower morbidity than re-irradiation (re-RT). Extended resections are feasible only in selected cases. Re-RT, particularly with hyperfractionated intensity-modulated RT or proton therapy, remains essential for unresectable tumors but demands careful patient selection and vigilant management of late adverse effects. Systemic therapy has uncertain benefit, while immunotherapy is mostly effective in metastatic disease. Nodal recurrence is mainly managed with neck dissection, with prognosis shaped by extranodal spread and recurrence type.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".