Bibliographic record
Abstract
We would like to thank Tao et al. [1] for their interest in our article [2]. As the authors of the letter point out, our study clearly demonstrates that preoperative anaemia is an important risk factor for morbidity and mortality after aortic valve surgery. While cardiac surgical procedures can be safely performed in this population, anaemic patients experience perioperative complications more frequently compared with their non-anaemic counterparts. Our study highlighted some of these issues and also identified important knowledge gaps, as indicated below:In conclusion, preoperative anaemia represents a potentially important therapeutic target for optimization in the patient undergoing cardiac surgery. Preoperative anaemia is a common finding in patients undergoing aortic valve surgery, with almost one-third of those patients being anaemic. Although there are many potential causes for anaemia in this population [3], further prospective studies are required in order to further define the aetiology of anaemia and the potential interventions to treat it. Preoperative anaemia is an important risk factor for both morbidity and mortality after aortic valve surgery, as found in our study and others' [4]. However, it is unclear whether preoperative anaemia can be corrected without the administration of allogenic blood products. Furthermore, it is unclear whether the improvement in preoperative haemoglobin would result in a reduction in perioperative morbidity and mortality. In elective patients with preoperative anaemia, appropriate identification and treatment of anaemia may lead to an improved outcome. This hypothesis needs to be validated in prospective clinical trials.
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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 | 0.046 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.036 | 0.039 |
| 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".