Effect of Primary Tumour Volumes in Early T-Stage Nasopharyngeal Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between primary tumour volumes and treatment outcomes in early T-stage nasopharyngeal carcinoma. DESIGN: Retrospective study. SETTING: Tertiary care centre. METHOD: A consecutive series of 52 newly diagnosed patients of early T-stage nasopharyngeal carcinoma who were treated with high-dose radiotherapy. MAIN OUTCOME MEASURES: Computed tomography-derived primary tumour volume was obtained following the summation of area technique. The cancer-related survival according to T stage and primary tumour volumes was analyzed. RESULTS: The median primary tumour volume was 5.48 mL in T1 disease, 17.95 mL in T2a disease, and 19.15 mL in T2b disease, with a range of 3.23 to 9.65 mL in T1 disease, 6.31 to 64.54 mL in T2a disease, and 11.27 to 131.82 mL in T2b disease. Large primary tumour volume was associated with a significantly poor disease-specific survival (p = .0003), whereas the T stage that segregated into T2a and T2b carried no prognostic significance (p = .441). CONCLUSIONS: A substantial variation of primary tumour volume was present within the T2a and T2b stages, and primary tumour volume represented a more important prognostic factor. Volumetric measurements of primary tumours in early T-stage nasopharyngeal tumours would better refine the tumour, node, metastasis staging system. Patients with large primary tumour volume should be treated more aggressively.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".