Bibliographic record
Abstract
We thank Anastasiadis and colleagues [1] for their thoughtful comments on our paper entitled ‘Cerebral oximetry and preventing neurological complication post-cardiac surgery: a systematic review’ [2]. This review provided an up-to-date synopsis of observational and interventional studies assessing the role of near infrared spectroscopy (NIRS) derived cerebral oximetry in preventing adverse neurological outcomes after cardiac surgery. We agree with the authors that, despite the limitations of NIRS, this technology has the potential to be a valuable tool for monitoring cerebral perfusion during cardiac surgery. It is a non-invasive and continuous surrogate measure of regional oxygen saturation. Moreover, our systematic review highlights a number of studies that support the use of NIRS cerebral oximetry in preventing postoperative neurological impairment in the form of delirium, post-operative cognitive decline (POCD) and/or stroke [3, 4]. Given that patients undergoing cardiac surgery continue to present with postoperative neurological complications, monitoring cerebral perfusion during surgery may act as a strategy to improve upon these outcomes. However, we urge caution concluding that poor cerebral oxygenation is definitively related to postoperative neurological complications. Similar caution should be made determining that intervening on low NIRS values reduced incidence of delirium, POCD or stroke. Our review demonstrates that there are nearly equivalent number of studies supporting and refuting the association of NIRS with neurological complications as well as the effect of optimizing NIRS to prevent such adverse outcomes. Our review identified key sources of variability in these studies and suggested possible solutions to improve study comparability. For example, assessments used to detect POCD should equally encompass all cognitive domains. Instead of using absolute NIRS measures, relative changes in cerebral perfusion or autoregulation may also provide more consistent data in this field. Finally, standardizing time points of assessment will also provide insight on the trajectory of recovery for each patient. Identifying sources of variability that impact the association between rSO2 and postoperative neurological outcome are of paramount importance in moving this field forward. We agree with Anastasiadis et al. in that ‘there’s smoke’, and there may be fire. However, just as in putting out a fire, you need to understand the cause thoroughly first. We worry that designing interventions to change rSO2 during cardiac surgery before completely understanding the complexities of the problem may have similar unintended consequences. Pouring water on a grease fire will only make things worse.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.040 | 0.042 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".