How to use natriuretic peptides in non-cardiac surgery
Bibliographic record
Abstract
Journal Article Accepted manuscript How to use natriuretic peptides in non-cardiac surgery Get access Emmanuelle Duceppe, MD PhD, Emmanuelle Duceppe, MD PhD Department of Medicine, University of Montreal, Montreal, QC, CanadaCentre Hospitalier de l'Université de Montréal, Montréal, QC, CanadaPopulation Health Research Institute, Hamilton, ON, Canada Corresponding author: Dr. Emmanuelle Duceppe, 1000 rue St-Denis, Montreal, QC, H2X 0C1, CANADA, Email: emmanuelle.duceppe.med@ssss.gouv.qc.ca Search for other works by this author on: Oxford Academic Google Scholar Nicholas L Mills, MBChB, PhD, Nicholas L Mills, MBChB, PhD BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United KingdomUsher Institute, University of Edinburgh, Edinburgh, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Christian Mueller, MD, Christian Mueller, MD Cardiovascular Research Institute Basel (CRIB) and Department of Cardiology, University Hospital Basel, University of Basel, Switzerland https://orcid.org/0000-0002-1120-6405 Search for other works by this author on: Oxford Academic Google Scholar Study Group on Biomarkers of the ESC Association for Acute Cardiovascular Care Study Group on Biomarkers of the ESC Association for Acute Cardiovascular Care Search for other works by this author on: Oxford Academic Google Scholar European Heart Journal. Acute Cardiovascular Care, zuae038, https://doi.org/10.1093/ehjacc/zuae038 Published: 26 March 2024 Article history Received: 19 March 2024 Accepted: 20 March 2024 Published: 26 March 2024
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".