Absolute risk and cost of management of toxicities of newly approved anticancer drugs: A meta-analysis.
Bibliographic record
Abstract
e17556 Background: Frequency of rare but serious adverse events (AEs) caused by new anticancer drugs, and costs associated with their management, are poorly documented. The type of anticancer drug can influence the prevalence of such AEs and associated costs. Methods: We identified anticancer drugs andpivotal trials supporting their registration by Food and Drug Administration during 2000-2011. Twelve frequent, grade III-IV AEs were weighted and pooled in a meta-analysis. Incremental drug prices, and costs for management of AEs were estimated according to target-specificity of new agents and control treatment used in the trials. Results: We identified 41 studies (27,500 patients; 6,000 events) involving 19 experimental drugs. Agents directed against a specific molecular target on cancer cells had trend towards lower incidence of grade III-IV toxicities than the controls (median relative risk [RR]=0.7, p=0.2), whereas less-specific targeted agents, including angiogenesis-inhibitors (median RR=3.4, p<0.001), and chemotherapeutic agents (median RR=1.6, p<0.01) were more toxic. Risk was increased regardless of whether control arm contained active treatment (RR=2.1, p<0.001) or placebo (RR=3.0, p<0.001). Median incremental drug-price for experimental agents was $6000/patient/month. Cost of toxicity management was lower than controls if experimental agents were specific but was higher for less-specific targeted agents and chemotherapies. Conclusions: Newly approved anticancer drugs are associated with increased toxicity except for agents with a specific molecular target on cancer cells. Management of toxicity leads to a relatively small increase in overall cost of treatment. Frequency of toxicity and associated costs are likely higher in less selected patients treated in general oncologic practice.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.058 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".