Individualizing and Minimizing Sedation on Venovenous Extracorporeal Membrane Oxygenation in Acute Respiratory Distress Syndrome Patients, a Reply
Bibliographic record
Abstract
To the Editor: We thank Shekar et al.1–3 for their interest in our study; and commend them for their important research regarding the pharmacokinetic and pharmacodynamic changes induced by the extracorporeal membrane oxygenation (ECMO) circuit. Our observational study of patients managed in an experienced ECMO center illustrated wide variability in sedation depth, from deeply sedated to awake and interactive. Although sedation depth is obviously dependent on patient and clinician factors, it appears possible for patients to be awake, comfortable, interactive, and mobilizing during ECMO. In non-ECMO patients, light sedation confers advantages such as patient inter-activeness and active mobilization, and has been shown to improve outcomes in critically ill patients.4–7 Keeping patients awake also promotes an individualized patient-centered approach, whereby patients can communicate their symptoms and receive the appropriate pharmacologic and nonpharmacologic interventions for pain, anxiety, and insomnia. In patients on ECMO, we agree that the benefits of light sedation must be weighed against the safety risks, including hemodynamic, respiratory, and device-removal concerns. It should be noted that the few patients who could sit, stand, or walk with the ECMO circuit did so safely, with continuous monitoring and the support of multiple personnel. Serious complications (such as decannulation) in our cohort were absent. Given the safety concerns, we recommend a cautious approach, with gradual lightening of the patient’s sedation to evaluate her tolerance. There are no prospective trials evaluating dexmedetomidine in patients on ECMO; data in non-ECMO patients does not show advantages compared with conventional sedatives, and the cost is prohibitive in many centers. We agree with the comments by Shekar et al.1–3 regarding early tracheostomy, the use of adjunctive enteral medications, and the need for prospective studies evaluating light sedation in this population. Julian deBackerCleveland Clinic Lerner College of MedicineCleveland, OhioDepartment of Medicine and Interdepartmental Division of Critical Care MedicineMount Sinai HospitalToronto, Ontario, Canada Eddy FanDepartment of Medicine and Interdepartmental Division of Critical Care MedicineUniversity Health Network and University of TorontoToronto, Ontario, Canada Sangeeta MehtaDepartment of Medicine and Interdepartmental Division of Critical Care MedicineMount Sinai Hospital and University of TorontoToronto, Ontario, Canada
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".