Surveillance tools for detection of recurrent nasopharyngeal carcinoma: An evidence‐based review and recommendations
Bibliographic record
Abstract
Abstract Objective Nasopharyngeal carcinomas (NPC) are tumors arising from epithelium of the nasopharynx. The 5‐year survival rate of primary NPC is 80% with significant risks of recurrence. The objective here is to provide an evidence‐based systemic review of the diagnostic value of different modalities in detecting local, regional, and distal recurrent NPC, as well as the associated costs with these modalities. Methods MEDLINE, EMBASE, and the Cochrane review database were queried. Two hundred and twenty‐three abstracts were generated using the inclusion criteria: patients >18 years of age; histopathological reference standard; and modalities pertaining to imaging or microbiology. Results Twenty‐four manuscripts fulfilled the inclusion criteria and 5 surveillance tools identified: endoscopy, MR, FDG‐PET, Tc‐99m MIBI and 201TI SPECT, and EBV DNA. Conclusions For local surveillance, endoscopy is the gold standard recommendation, with increased efficacy if Narrow Band Imaging or contact endoscopy are utilized. MRI and FDG‐PET is also recommended to help with local to distal spread; however, Tc‐99m MIBI and 201TI SPECT are options as well. EBV DNA is recommended as a cheap and accessible adjunct surveillance tool if an available as an option.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".