Bibliographic record
Abstract
In Response: We appreciate Shander and Moskowitz's concern that the evidence regarding hydroxyethyl starch 264/0.45 (HES 264/0.45) and its clinical effect on bleeding is inconclusive. They point out many of the difficulties in establishing a causal link between the known effects of hydroxyethyl starches on coagulation factors and the risk of bleeding during surgery. As we point out in our paper, the purpose of our study was to compare the effect of large volumes of HES 264/0.45 to albumin on various coagulation parameters. Our study was adequately powered to compare the study colloids with respect to these end-points. In the discussion we explain our concern that the observed increased transfusion rate in the HES 264/0.45 group may be a spurious result because of the small sample size or the imbalance in gender allocation that occurred by chance. Our results support the continued use of HES 264/0.45. However, we feel that the results of this study provide additional impetus for larger adequately powered trials examining the use of IV colloids with allogeneic blood transfusion as the primary outcome. Until the results of such studies are available, controversy will continue regarding the use of HES and the risk of bleeding. Ramiro Arellano, MD, MSc Department of Anesthesiology Queen's University Kingston, Ontario, Canada [email protected]
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".