Reply to Eiland et al
Bibliographic record
Abstract
To the Editor— Eiland et al. [1] raise several points with regard to our article [2]. Specifically, they outline 3 issues. First, they quite correctly point out that the prevalence of Candida parapsilosis isolated in the caspofungin arm of the study was statistically different from that seen in both micafungin arms of the study. This may have hindered the success rate in the caspofungin arm relative to the 2 micafungin arms. Second, they note a disparity among the study groups with regard to the number of diabetic patients and of patients who had undergone recent surgery. Finally, they raise a question about our methods of stratification, which were based on region and APACHE II score. Eiland et al. [1] would have preferred a randomization scheme based on the source of the enrollee (clinic or insti-tution). We acknowledge that there were differences in the numbers of patients infected with C. parapsilosis in the caspofungin group relative to the number in the micafungin arms. Moreover, the response rates for that species differed as well: 22 (75.9%) of 29 patients in the 100-mg micafungin arm, 15 (71.4%) of 21 patients in the 150-mg micafungin arm, and 27 (64.3%) of 42 patients in the caspofungin arm responded to treatment. We have attempted to address this concern by reanalyzing the response data after removal of the C. parapsilosis– infected patients from each arm of the study; the resulting success rates were as follows: 124 (76.5%) of 162 patients in the 100-mg micafungin arm, 127 (71.3%) of 178 in the 150-mg micafungin arm, and 109 (74.7%) of 146 patients in the caspofungin arm. In this analysis, the bias imposed by excessive numbers of C. parapsilosis isolates has been removed, and the resulting evaluation still indicates that the micafungin treatment groups are noninferior to caspofungin within the 15% margin (95% CI for the differences in the 2 micafungin groups were − 7.3% to 11.3% and − 11.9% to 6.8%, respectively), maintaining statistical power close to 90% when applying Hochberg multiple comparisons methods.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.033 | 0.040 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".