Analysis of 2167 head and neck cancer patients' management, treatment compliance and outcomes from a regional cancer centre, Delhi, India
Bibliographic record
Abstract
Head and neck cancer care was analysed in 2167 unselected patients for management compliance and outcome. Median age was 55 years, with a male to female ratio of 5.5ratio1. Major sites were oropharynx (32.4 per cent), larynx (19.8 per cent), oral (16.6 per cent) and hypopharynx (12.9 per cent). Stage-wise distribution was I-II=8.9 per cent, III=20.6 per cent and IV=60.3 per cent and unstaged=10.2 per cent. Squamous cell carcinoma was the dominant histology for 90.9 per cent. Clinic-based cancer-directed treatment decisions were made for 1905 patients: curative intent in 53 per cent, palliative in 35 per cent and for the remaining 262 (12 per cent) supportive care. Overall, 1209 (56 per cent) patients complied with the prescribed treatments; 62 per cent, 54 per cent, and 35 per cent of curative, palliative and supportive care intent groups, respectively. Modalities were radiotherapy alone (64.6 per cent), combined surgery with irradiation (17.6 per cent), and chemoradiotherapy (11.2 per cent). Median follow-up periods were 17.5 and three months in curative and palliative groups respectively. Overall, 712 (33 per cent) cases received curative therapy, with three-year disease-specific survival of 49 per cent. Patient compliance was a major obstacle. The comparison of this series with the USA, Canada and Norway showed wide disparities in stage of presentation and survival.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".