A Bayesian Meta-analysis of Prophylactic Granulocyte Colony-Stimulating Factor and Granulocyte-Macrophage Colony-Stimulating Factor in Children with Cancer
Bibliographic record
Abstract
The purpose of this analysis was to examine the efficacy of prophylactic hematopoietic colony-stimulating factors (CSFs) in pediatric cancer and to describe how a Bayesian meta-analysis can be conducted and then modified to incorporate information not readily included in a frequentist meta-analysis. Three Bayesian models were developed. The simplest model used the same data as a published frequentist meta-analysis. The second model included data that could not easily be incorporated into the frequentist meta-analysis, including data from different courses of chemotherapy and continuous outcomes that did not report variance estimates. The third model examined the effect of CSF type (granulocyte CSF vs. granulocyte-macrophage CSF). Compared with the frequentist model, the Bayesian model with the most data suggested a greater benefit of CSFs, with a 3.2-day reduction in duration of parenteral antibiotics (95% credible interval: -7.1, 0.7) in the expanded Bayesian model compared with a 0.8-day (95% confidence interval: -2.3, 0.7) reduction in the frequentist model. Bayesian meta-analysis also suggested that, compared with granulocyte-macrophage CSF, granulocyte CSF was associated with a 4.8-day decrease in the duration of parenteral antibiotics. Bayesian meta-analysis can readily include information not easily incorporated in a frequentist meta-analysis. Some treatment effect estimates were larger by a clinically important amount when additional data contributed to the pooled estimate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".