Nimotuzumab in the treatment of patients with locally advanced HPV-negative oropharyngeal cancer in Indian setting: A health-economic evaluation
Bibliographic record
Abstract
ABSTRACT Background: The addition of nimotuzumab to concurrent chemoradiotherapy (CTRT) has improved outcomes, including progression-free survival (PFS) and overall survival (OS) in human papilloma virus (HPV)-negative oropharyngeal cancers (OPC). Objectives: This study primarily assessed the cost-effectiveness of nimotuzumab plus CTRT versus CTRT alone in HPV(-) OPC in India by estimating the incremental cost-effectiveness ratio (ICER). The secondary objectives included comparing clinical outcomes, including life-years gained and quality-adjusted life years (QALYs), between the two strategies. Materials and Methods: A decision-tree model was developed in Microsoft Excel to assess the cost-effectiveness of nimotuzumab plus CTRT versus CTRT alone for HPV(-) OPC. Health states included locoregional control (LRC), adverse drug events (ADEs), and PFS. The model, from the Indian payer’s perspective, used a 2-year time horizon and applied a 3% annual discount rate. Primary outcomes were ICER and QALYs. Model robustness was confirmed through probabilistic sensitivity analysis (PSA). Results: In the base-case analysis, total costs and QALYs for nimotuzumab plus CTRT versus CTRT alone were ₹9,91,872 versus ₹8,75,180 and 1.262 versus 0.628, respectively. The incremental cost and QALY gain with nimotuzumab were ₹116,692 and 0.634, respectively. The ICER was ₹183,952 per QALY, well below the willingness-to-pay threshold of ₹300,000 per QALY, indicating cost-effectiveness. Net monetary benefit (NMB) and net health benefit (NHB) associated with nimotuzumab were ₹73,508 and 0.245, respectively. Conclusion: Nimotuzumab plus CTRT is a cost-effective option compared with CTRT alone for treating HPV(-) OPC in the Indian healthcare setting.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".