Bibliographic record
Abstract
We thank Wu and Grunkemeier for their insightful comments [1] regarding our manuscript that compared mechanical and bioprosthetic valve replacement in middle-aged patients [2]. Cumulative incidence (actual) analysis is an important statistical technique for use in the analysis of events other than death after valve replacement, and we have employed this technique in our previous work [3]. Drs Wu and Grunkemeier are to be applauded for their pioneering work in this field [4]. In studying the outcomes after valve replacement in middle-aged patients, we performed both actuarial and cumulative incidence (actual) analysis. However, we chose to present only the actuarial analysis in the final manuscript for several reasons. Firstly, we are aware of the recent string of editorials and letters to the editor passionately debating the use of cumulative incidence (actual) analysis. Two other prominent cardiac surgery journals have issued a moratorium on the publication of ‘actual freedom’ results [5]. Secondly, as Drs Wu and Grunkemeier have pointed out, the difference between the results from actuarial and cumulative incidence (actual) analysis increases as the age of the cohort increases [4]. Thus, for a relatively young (middle-age) population with a low competing risk of death, we believe that employing only actuarial analysis is generally sufficient. Finally, but most importantly, both the actuarial and cumulative incidence (actual) techniques led to similar results and conclusions in this study. Therefore, for the purpose of simplicity, and in order to avoid the controversial debate, we reported only the actuarial analysis in the manuscript.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.052 | 0.083 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".