Bibliographic record
Abstract
Annick Desjardins, MD, FRCPC, speaks to Roshaine Gunawardana, Managing Commissioning Editor: Annick Desjardins is Associate Professor within the Department of Neurology and is the Director of Clinical Research at The Preston Robert Tisch Brain Tumor Center at Duke. In 2003, Dr Desjardins completed her residency in Adult Neurology at the Universite de Sherbrooke, Quebec, Canada. Following a 2-year fellowship in neuro-oncology at The Preston Robert Tisch Brain Tumor Center at Duke, she joined the Center as faculty, in July 2005. She is a Fellow of the Royal College of Physicians of Canada. She has been the Principal Investigator on over 30 therapeutic trials in neuro-oncology, including investigator initiated and international multicenter studies, and has held several Investigational New Drug applications. She has over 80 peer-review publications and six book chapters. She has written invited expert reviews for Hospital Pharmacy Europe, Nature Reviews Neurology, Clinical Care Options and MEDscape CME. She is reviewer for Neuro-Oncology, Cancer, Journal of Neuro-Oncology, Clinical Cancer Research, Expert Review of Anticancer Therapy, Cancer Research, Molecular Cancer Therapeutics and Future Oncology.
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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.099 | 0.054 |
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".