Bibliographic record
Abstract
We thank Son et al. [1] for their interest in our article [2] and for the points they raised. The authors bring up an issue that is both important and controversial, i.e. the impact of gender on outcomes after aortic valve surgery as well as the possible differential effect of anaemia in males versus females. Female gender has been included as a factor in many risk-scoring systems including the EuroSCORE [3]. However, several studies have found that female gender is not a risk factor for poor outcomes after aortic valve surgery. Fuchs et al. [4] studied the gender differences of patients undergoing aortic valve replacement (AVR) for isolated severe aortic stenosis and found that although women referred to AVR are older and more symptomatic, gender did not impact on operative and long-term mortality. In the oldest age group of 79 years and older, women even have a better outcome, presumably due to a longer mean life expectancy. However, it is important to differentiate the effect of gender as a confounder (affecting the relationship between anaemia and outcomes) and as an effect modifier (having a differential effect of anaemia on outcomes based on gender). In order for gender to be a significant confounder, it would have to be associated with either the exposure (anaemia) or outcomes (mortality and morbidity). In our study, we did not find gender to be significantly associated with either variable, and therefore, by definition, gender was not a significant confounder of the relationship between anaemia and outcomes. Furthermore, addition of gender to our multivariable model did not significantly change the effect of anaemia on outcomes. The threshold effect of anaemia on outcomes was observed for the entire cohort and was similar between males and females. Son et al. [1] also propose the notion that females may have a better tolerance to haemodilution than males. Our study was not designed to answer this question, but there is a lack of convincing evidence supporting this phenomenon in the literature. In our study, the impact of anaemia on outcomes was observed equally in males and females [2]. Son et al. [1] also propose that concomitant coronary artery bypass grafting surgery (CABG) surgery may be a confounder. Similar to gender, concomitant CABG was equally distributed within our exposure groups and addition of this variable to our model did not impact on the relationship between exposure and outcomes, suggesting that it was not a significant confounder in our population.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.042 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".