Primary Tumour Volume: Important Predictor of Outcome for T3- and T4-Staged Nasopharyngeal Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between primary tumour volumes and treatment outcomes within T3- and T4-staged nasopharyngeal carcinoma. DESIGN: Retrospective study. SETTING: Tertiary care centre. METHODS: Forty newly diagnosed T3-staged patients and 36 newly diagnosed T4-staged patients participated in the study. MAIN OUTCOME MEASURES: Computed tomography-derived primary tumour volume was obtained from the summation of areas technique. The probabilities of achieving tumour control and patient survival were estimated using the product-limit method of Kaplan and Meier. The log rank test was used to examine significance. RESULTS: In T3-staged nasopharyngeal carcinoma, the median primary tumour volume was 29.6 mL, with a range from 8.0 to 131.8 mL. After segregating the primary tumour volume into two subgroups (< 30 mL, > 30 mL), large primary tumour volume was associated with a significantly poorer disease-specific survival (p = .0001). In T4-staged cases, the median primary tumour volume was 54.07 mL, with a range from 6.7 to 223.1 mL. After segregating the primary tumour volume into two subgroups (< 60 mL, > 60 mL), larger primary tumour volume was associated with a significantly poorer disease-specific survival (p = .0022). CONCLUSION: Within the same T3- and T4-staged nasopharyngeal carcinoma, the primary tumour volume represented an important prognostic factor. To improve the treatment outcome of T3- and T4-staged nasopharyngeal carcinoma with large primary tumour volumes, more aggressive treatment is needed.
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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 | 0.002 |
| 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".