Bibliographic record
Abstract
Abdelsalam Elshaikh, MD, has joined the Division of Vascular Medicine. Dr. Elshaikh is a graduate of the University of Gezira Faculty of Medicine in Sudan. He then went on to complete an internal medicine internship at Beaumont Health in Dearborn, MI, and his internal medicine residency at St. Luke’s University Health Network in Bethlehem, PA. Following residency, he worked as a hospitalist for two years before completing a vascular medicine fellowship at the University Hospitals of Cleveland Medical Center. He cares for patients at the Jefferson Vascular Center in Center City.\nRakesh M. Suri, MD, DPhil, has joined the Division of Cardiac Surgery. Dr. Suri received his medical degree from the University of Toronto. He then went on to Magdalen College, Oxford, where he earned his D. Phil in immunology. Dr. Suri then went back to the University of Toronto to complete his general surgery residency and a fellowship in thoracic and cardiac surgery followed by a thoracic surgery residency at the Mayo Clinic in Rochester, MN. In addition to his clinical education, Dr. Suri also completed the Harvard Business School’s General Management Program in 2016. He is working with Jefferson Health leadership on special projects in innovation and international relations. He cares for patients at Thomas Jefferson University Hospital.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.718 | 0.503 |
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".