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Record W4403036158 · doi:10.1080/10428194.2024.2402814

Real-world outcomes following ibrutinib dose reduction in patients with chronic lymphocytic leukemia/small lymphocytic lymphoma

2024· article· en· W4403036158 on OpenAlexfundno aff
Mazyar Shadman, Monika Salkar, Bhavini Srivastava, Sudeep Karve, Bruno Émond, Priyanka Gogna, Ameur M. Manceur, Marie-Hélène Lafeuille, Andrew Rava, Haiyan Sun, Amanda Howarth, Samantha Tomicki, Barnabie Agatep, Barton Jones, Erin Franceschini, Chadi Saifan, Shaffee Bacchus, Lindsey E. Roeker, Deborah M. Stephens

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institutes of HealthAbbVie CanadaPharmacyclicsGlaxoSmithKlinePfizer
KeywordsIbrutinibChronic lymphocytic leukemiaMedicineLymphomaInternal medicineOncologyLeukemia

Abstract

fetched live from OpenAlex

This study used real-world data from three separate United States (US) databases to evaluate dosing patterns and time to next treatment (TTNT) following the first-incident adverse event (AE) in patients with chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) treated with first-line ibrutinib with and without dose reduction (DR). Median TTNT or death in patients with and without a DR following an AE in each database was as follows: Optum Clinformatics Data Mart (CDM): 59.5 and 30.6 months; ConcertAI: 27.1 and 18.0 months; and Medicare Fee-for-Service (FFS): 49.8 and 22.0 months, respectively. Median TTNT or death in patients with cardiac AEs, with and without a DR, was: Optum CDM: 44.4 and 22.9 months; ConcertAI: 29.9 and 18.3 months; and Medicare FFS: 49.6 and 14.0 months, respectively. Ibrutinib DR was associated with fewer outpatient visits and lower CLL/SLL-related medical costs. These findings suggest that utilizing ibrutinib DR may effectively manage tolerability without compromising clinical efficacy.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.013
GPT teacher head0.270
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations8
Published2024
Admission routes1
Has abstractyes

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