Bibliographic record
Abstract
Anne-Marie Guerguerian MD PhD is a Pediatric Intensive Care Physician in the Department of Critical Care Medicine of the Hospital for Sick Children in Toronto, Canada, where she serves as the Medical Director of the Extracorporeal Life Support Program. As a Senior Scientist of the Research Institute, her program of research is focused on developing methods to quantify brain injury during critical illness. At the University of Toronto, she is appointed to the Department of Paediatrics, the Interdepartmental Division of Critical Care Medicine, and to the Institutes of Medical Sciences, Biomedical Engineering, and Health Policy and Management. As a Clinician Scientist, she volunteers for the Extracorporeal Life Support Organization, and pediatric research resuscitation task forces of the Heart and Stroke Foundation of Canada, the International Liaison Committee on Resuscitation, and for the Get With The Guidelines’ American Heart Association.Dr. Lakshmi Raman, an Associate Professor of Pediatrics, is currently the Medical Director of the Pediatric and Neonatal Extracorporeal Life Support (ECLS) program at Dallas Children’s Medical Center in Dallas Texas, an ELSO center of Excellence. She is currently the Chair of the Publications committee at ELSO which oversees the Guidelines and protocols. Dr. Raman’s research interest is, understanding neurological injuries on extracorporeal membrane oxygenation (ECMO). Dr. Raman has authored more than 30 peer-reviewed publications and currently directs an ELSO-endorsed educational course to teach ECLS in Dallas, Texas.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.563 | 0.424 |
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".