Impact of age on treatment effect in locally advanced head and neck cancer (HNC): Two individual patient data meta-analyses
Bibliographic record
Abstract
5501 Background: The Meta-Analysis of Radiotherapy in Carcinomas of Head & Neck (MARCH; Bourhis J, ASTRO 2002) showed that altered fractionation radiotherapy (Alt-RT) could improve survival as compared to standard RT in patients with locally advanced HNC (pooled hazard ratio - HR -: 0.92, 95% confidence interval: 0.86–0.97). The Meta-Analysis of Chemotherapy in Head and Neck Cancer (MACH-NC; Bourhis J, ASCO 2004) demonstrated that concomitant chemotherapy (CT), added to RT, improved survival (HR: 0.82, 95% CI: 0.78–0.86). This study considers age as a potential modifier of the treatment effect. Methods: 15 randomized trials with 6,515 patients were included in MARCH (median follow up: 6.0 years), and 50 with 9,471 patients in concomitant part of MACH-NC (median follow up: 5.6 years). The interaction between age and treatment effect, using HR of death, was tested with heterogeneity test. Effect of prognostic factors on the interaction was analysed using Cox model. Results: The effect of Alt-RT in MARCH and of concomitant CT in MACH-NC on overall survival decreased with increased age ( table ). Patients aged 71+ had a lower performance status, less advanced stage, and more often laryngeal cancer than the younger patients; there were more women in the oldest patients group. However, adjusting on covariates did not modify the results. Causes of death was available in MARCH and in recent (1994–2000) trials of MACH-NC. The proportion of deaths not due to HNC increased with age, from 18% at age 50 to 41% at age 71+ in MARCH, and from 15% to 39% in MACH-NC. Conclusions: Treatment benefit decreases with increasing age. Patients aged 71+ did not benefit from Alt- RT nor from concomitant CT. The increasing risk of death from other causes with age may explain part of these observations. Supported by PHRC, ARC, LNCC [Table: see text] No significant financial relationships to disclose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".