Correction: Inotuzumab ozogamicin in pediatric patients with relapsed/refractory acute lymphoblastic leukemia
Bibliographic record
Abstract
We thank the research coordinators and following physicians at pediatric cancer centers for contributing data to this project: Prashant Hiwarkar and Jayashree Motwani, Birmingham Women's and Children's Hospital, UK; Kelly Malone, Children's Hospital of Colorado, USA; Mylene Bassal, Children's Hospital of Eastern Ontario, Canada; Yoav Messinger and Joanna Perkins, Children's Hospital of Minnesota, USA; Van Huynh, Children's Hospital of Orange County, USA; Richard Ho, Children's Hospital at Vanderbilt, USA; Joanne Chuah and Jessa Morales, Children's Hospital at Westmead, Australia; Donald Wells, Dell Children's Hospital, USA; Nicolas Boissel, Hospital Saint-Louis, France; Tannie Huang, Kaiser Permanente, USA; Stacey Marjerrison, McMaster Children's Hospital, Canada; William Carroll and Joanna Pierro, New York University Langone Medical Center, USA; Ajay Vora, Sheffield Children's Hospital, UK; Donna Lancaster, The Royal Marsden Hospital, UK; Lucie Šrámková, University Hospital Motol, Czech Republic; Chatchawin Assanasen, University of Texas Health Science Center, San Antonio, USA; Rupert Handgretinger, University of Tübingen, Germany.
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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 | 0.004 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".