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Record W4402149322 · doi:10.1038/s41375-024-02385-6

Correction: Dose modification dynamics of ponatinib in patients with chronic-phase chronic myeloid leukemia (CP-CML) from the PACE and OPTIC trials

2024· erratum· en· W4402149322 on OpenAlexaff
Elias Jabbour, Jane F. Apperley, Jörge E. Cortes, Delphine Réa, Michael W. Deininger, Elisabetta Abruzzese, Charles Chuah, Daniel J. DeAngelo, Andreas Hochhaus, Jeffrey H. Lipton, Michael J. Mauro, Franck E. Nicolini, Javier Pinilla‐Ibarz, Gianantonio Rosti, Philippe Rousselot, Neil P. Shah, Moshe Talpaz, Alexander Vorog, Xiaowei Ren, Hagop M. Kantarjian

Bibliographic record

VenueLeukemia · 2024
Typeerratum
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsPonatinibMyeloid leukemiaMedicineChronic myeloid leukaemiaOncologyInternal medicineNilotinibImatinib

Abstract

fetched live from OpenAlex

Following publication of the article, the authors realized that several data points in Figure S3B and the accompanying manuscript text were incorrect. This oversight has been addressed and the corrected figure has been added. 1. Supplementary information , Figure S3B: The percentage of patients with grade 3-4 TE-AOEs from the PACE CP-CML group was inaccurately rounded to 12%; the correct percentage is 11%. In addition, we have corrected the error in the manuscript on page 477 in the Safety outcomes section. The revised statement is below: Grade 3-4 TE-AOEs were 11% in PACE and 5% in OPTIC, and serious TE-AOEs were 15% in PACE and 4% in OPTIC (Fig. S3 ). 2. Supplementary information , Figure S3B: The number of patients with serious TE-AOEs in the PACE CP-CML group was cited incorrectly as 9; the correct number of patients is 40.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.171
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.171
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0600.030

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.

Opus teacher head0.020
GPT teacher head0.294
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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