Correction: “The 5th edition of The World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms” Leukemia. 2022 Jul;36(7):1720–1748
Bibliographic record
Abstract
At the time of original publication, there was an incomplete listing of contributing authors and their institutions. This revision corrects those omissions. 1. Dr Ritsuro Suzuki, Department of Hematology & Oncology, Shimane University School of Medicine, Shimane, Japan, was regrettably omitted from the main author list; 2. Dr Arianna Di Napoli, Department of Clinical and Molecular Medicine, Sapienza University, School of Medicine and Psychology, Sant’Andrea Hospital, Rome, Italy, was incorrectly included as a contributor in the author contribution list, and instead should be a coauthor in the main author list; 3. Dr Luis M. Allende, a contributor in the author contribution list, was assigned with an incorrect affiliation, and the correct affiliation is 70 Immunology Department, Hospital 12 de Octubre, imas12, Madrid, Spain; 4. Dr Aanchal Kakkar, a contributor in the author contribution list, was assigned with an incorrect affiliation, and the correct affiliation is Pathology, All India Institute of Medical Sciences, New Delhi, India.
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 | 0.004 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.041 | 0.031 |
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".