International Recommendations on Postoperative Management for Potentially Resectable Locally Recurrent Nasopharyngeal Carcinoma
Bibliographic record
Abstract
Locally recurrent nasopharyngeal carcinoma (NPC) presents substantial challenges in clinical management. Although postoperative re-irradiation (re-RT) has been acknowledged as a potential treatment option, standardized guidelines and consensus regarding the use of re-RT in this context are lacking. This article provides a comprehensive review and summary of international recommendations on postoperative management for potentially resectable locally recurrent NPC, with a special focus on postoperative re-RT. A thorough search was conducted to identify relevant studies on postoperative re-RT for locally recurrent NPC. Controversial issues, including resectability criteria, margin assessment, indications for postoperative re-RT, and the optimal dose and method of re-RT, were addressed through a Delphi consensus process. The consensus recommendations emphasize the need for a clearer and broader definition of resectability, highlighting the importance of achieving clear surgical margins, preferably through an en bloc approach with frozen section margin assessment. Furthermore, these guidelines suggest considering re-RT for patients with positive or close margins. Optimal postoperative re-RT doses typically range around 60 Gy, and hyperfractionation has shown promise in reducing toxicity. These guidelines aim to assist clinicians in making evidence-based decisions and improving patient outcomes in the management of potentially resectable locally recurrent NPC. By addressing key areas of controversy and providing recommendations on resectability, margin assessment, and re-RT parameters, these guidelines serve as a valuable resource for clinical experts involved in the treatment of locally recurrent NPC.
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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".