Meta-analysis of chemotherapy in head and neck cancer (MACH-NC): An update on 107 randomized trials and 19,805 patients, on behalf of MACH-NC Group
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Meta-Analysis of Chemotherapy in squamous cell Head and Neck Cancer (MACH-NC) demonstrated that concomitant chemotherapy (CT) improved overall survival (OS) in patients without distant metastasis. We report the updated results. MATERIALS AND METHODS: Published or unpublished randomized trials including patients with non-metastatic carcinoma randomized between 1965 and 2016 and comparing curative loco-regional treatment (LRT) to LRT + CT or adding another timing of CT to LRT + CT (main question), or comparing induction CT + radiotherapy to radiotherapy + concomitant (or alternating) CT (secondary question) were eligible. Individual patient data were collected and combined using a fixed-effect model. OS was the main endpoint. RESULTS: For the main question, 101 trials (18951 patients, median follow-up of 6.5 years) were analyzed. For both questions, there were 16 new (2767 patients) and 11 updated trials. Around 90% of the patients had stage III or IV disease. Interaction between treatment effect on OS and the timing of CT was significant (p < 0.0001), the benefit being limited to concomitant CT (HR: 0.83, 95%CI [0.79; 0.86]; 5(10)-year absolute benefit of 6.5% (3.6%)). Efficacy decreased as patients age increased (p_trend = 0.03). OS was not increased by the addition of induction (HR = 0.96 [0.90; 1.01]) or adjuvant CT (1.02 [0.92; 1.13]). Efficacy of induction CT decreased with poorer performance status (p_trend = 0.03). For the secondary question, eight trials (1214 patients) confirmed the superiority of concomitant CT on OS (HR = 0.84 [0.74; 0.95], p = 0.005). CONCLUSION: The update of MACH-NC confirms the benefit and superiority of the addition of concomitant CT for non-metastatic head and neck cancer.
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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 | 0.033 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".