Pembrolizumab Weight-Based Dosing: Conviction and Lacunae in Adopting a Cost-Saving Approach—A Survey Report
Bibliographic record
Abstract
Abstract Introduction Use of immunotherapy drugs has increased leaps and bounds in the last decade with promising results in some of the cancers. The use is limited in low- and middle-income countries due to cost constraints. Weight-based dosing is one measure adopted by Canada and Israel to reduce cost burden and improve access to immunotherapeutic drugs. Objective We conducted a survey among medical oncologists from India to understand challenges faced in accepting the weight-based dosing of pembrolizumab. Materials and Methods Questionnaire covering various aspects related to use of immunotherapy drugs was made and it was circulated across various social media platforms. Medical oncologists practicing across India were invited to participate in this survey. The issues like access to drugs and awareness about weight-based dosing of pembrolizumab were covered in the survey. Also, the impact of international guidelines on accepting the weigh-based dosing was studied. Results Ninety-nine medical oncologists across India participated in the survey. Only 60% medical oncologists are aware about weight-based dosing of pembrolizumab practiced in other countries. Further, 70% of medical oncologists could not prescribe immunotherapy due to cost factor in majority (90%) of their patients. More than 90% agreed that they will use weight-based dosing of pembrolizumab if the Drug Controller General of India, National Comprehensive Cancer Network, or European Society of Medical Oncologists guidelines endorses weight-based dosing. Conclusion Weight-based dosing of pembrolizumab would be accepted if policy makers and Indian medical oncology societies come together and formulate guidelines. Such guidelines will improve accessibility to immunotherapy drugs and lead to huge cost savings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".