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Record W3175545552 · doi:10.1136/heartjnl-2021-319415

Response to ‘Adverse cardiac outcomes in patients with chronic myeloid leukaemia treated with tyrosine kinase inhibitors’

2021· letter· en· W3175545552 on OpenAlexaff
Darryl P. Leong, Christopher Hillis, Nazanin Aghel, Gregory R. Pond, Hsien Seow

Bibliographic record

VenueHeart · 2021
Typeletter
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineChronic myeloid leukaemiaTyrosine kinaseAdverse effectInternal medicineMyeloidMyeloid leukaemiaTyrosine-kinase inhibitorOncologyCancer

Abstract

fetched live from OpenAlex

The Authors’ reply We read with interest the correspondence by Papaila et al 1 concerning our work on cardiovascular outcomes among patients with chronic myeloid leukaemia (CML). They raise several important points. We agree that the characteristics of patients prescribed tyrosine kinase inhibitors (TKI) are likely to have evolved since the introduction of these transformative therapies. In our research, we found that prior to the regulatory approval of TKI, the incidence of adverse cardiovascular events among patients with CML was lower than among age-matched and …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes1
Has abstractyes

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