Conventional compared to network meta-analysis to evaluate antibiotic prophylaxis in patients with cancer and haematopoietic stem cell transplantation recipients
Bibliographic record
Abstract
Our purpose was to compare conventional meta-analysis and network meta-analysis to evaluate the efficacy of different prophylactic systemic antibiotic classes in patients undergoing chemotherapy or haematopoietic stem cell transplant (HSCT). We included randomised trials if patients had cancer or were HSCT recipients and the intervention was systemic antibacterial prophylaxis. Three types of control groups were used: (1) placebo, no antibiotic and non-absorbable antibiotic separately; (2) placebo and no antibiotic combined; and (3) all three combined. These gave different network geometries. Strategies synthesised were fluoroquinolone, trimethoprim-sulfamethoxazole, cephalosporin and parenteral glycopeptide versus control groups. In total 113 trials met the eligibility criteria. Where treatment effects could be estimated with both conventional and network meta-analysis, values were generally similar. However, where events were sparse, network meta-analysis could be more precise. For example, trimethoprim-sulfamethoxazole versus placebo for infection-related mortality showed a relative risk ratio (RR) of 0.55, 95% CI (0.21 to 1.44) with conventional, and RR 0.43, 95% credible region (0.20 to 0.82) with network meta-analysis. Cephalosporin versus fluoroquinolone was comparable only indirectly using the network approach and yielded RR 0.59, 95% credible region (0.28 to 1.20) to reduce bacteraemia. Incoherence (difference between direct and indirect estimates raising concerns about network meta-analysis validity) was observed with network geometry where control groups were separated, but not where control groups were combined. In this situation, conventional and network meta-analysis yielded similar results in general. Network meta-analysis results could be more precise when events were rare. Some analysis could only be performed with the network approach. These results identify scenarios in which network meta-analysis may be advantageous.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | medium |
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".