A glimpse into the black box of cost-effectiveness analyses
Bibliographic record
Abstract
Background Metastatic gastric cancer (GC) is an incurable and aggressive disease with a poor prognosis. Immunotherapy is an attractive approach for treating patients with cancer, and studies using immunotherapy have shown promising results in melanoma, kidney and non-small cell lung cancers, among others. Case presentation We present a case of a 50-year-old woman with metastatic GC whose cancer had progressed after first-line chemotherapy and who received pembrolizumab as an experimental treatment. Molecular analyses showed that her tumor was negative for PD-L1 expression, contained microsatellite stability and several focal somatic copy number alterations. The patient experienced an almost complete response after eleven cycles of treatment. Her symptoms related to the disease disappeared, and the medication was well tolerated. Conclusions Despite reports of promising responses in some patients, immunotherapy is not suitable for all patients; therefore, we explored the molecular characteristics that could explain the exceptional response and clinical benefits observed in our patient.
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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 | 0.028 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".