Bibliographic record
Abstract
We appreciate the letter by Bulatovic and Taneja on our study1 and agree that it would have been more accurate to state heparin dose in units rather than in milligrams in our algorithm. We also agree that heparin management, which encompasses heparin dosing, monitoring of effect, and reversal with protamine, is an important component of cardiac surgery that is incompletely understood and requires further investigation. Given that our algorithm was not aimed at optimizing or even modifying heparin management, we made no attempts to alter or audit heparin management practice. The milligram to milligram representation of the protamine to heparin dose is consistent with a low-dose protamine practice.2 Because heparin management at our institution was not altered with protocol implementation, this is not likely to have had an impact on our results.Our algorithm was aimed at optimizing coagulation management by incorporation of point-of-care coagulation testing into routine practice, and the results suggest that we succeeded in reducing transfusions and some adverse outcomes. We are looking forward to the results of our large, multicenter study to see whether our findings are generalizable (ClinicalTrials.gov Identified NCT02200419).Nevertheless, we do believe that additional benefits in coagulation management can be achieved by optimizing heparin management. We have noticed that in some of our patients who bleed unexpectedly, there is a profound deterioration in coagulation status, particularly platelet count and function, from rewarming to postprotamine periods, suggesting a contributory effect of protamine to the coagulopathy.3 Perhaps, these patients would not have bled if heparin management was optimized by, for example, using mathematical models4,5 or point-of-care heparin–protamine titration systems.6We therefore agree with Bulatovic and Taneja that systematic studies on heparin management in cardiac surgery are required, as we do not seem to be much ahead of where we were in the 1970s.7 Perhaps, with optimized heparin management, we can further improve hemostatic management of cardiac surgical patients and reduce the burden of perioperative coagulopathy.The authors have received research funding from Tem International GmBH (Munich, Germany) and Helena Laboratories (Beaumont, Texas), for an ongoing multicenter randomized trial of a point-of-care–based coagulation algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.058 | 0.043 |
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".