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Record W2982369322 · doi:10.1159/000500229

Safety-Related Postmarketing Modifications of Drugs for Hematological Malignancies

2019· article· en· W2982369322 on OpenAlexaff
Anat Gafter‐Gvili, Ariadna Tibau, Pia Raanani, Daniel Shepshelovich

Bibliographic record

VenueActa Haematologica · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicinePostmarketing surveillanceDrugAdverse effectOff-label useClinical trialFood and drug administrationPharmacovigilanceDrug approvalPharmacologyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

The prevalence of safety-related postmarketing label modifications of medications for hematological malignancies is unknown. We identified 35 new drugs indicated for hematological malignancies approved by the US Food and Drug Administration between January 1999 and December 2014. Characteristics of supporting trials and safety-related label modifications from approval to December 2017 were collected from drug labels. Regulatory review and approval pathways were also collected. New drug approvals were supported by trials with a median of 167 patients (interquartile range 115-316). All drugs were approved based on surrogate endpoints. Twenty-seven drug approvals (77%) were not supported by randomized controlled trials. All drugs received orphan drug designation, and most were granted fast track designation, priority review, and accelerated approval (83, 74, and 60%, respectively). A total of 28 drugs (80%) had postmarketing safety-related label modifications. Additions to black box warnings, contraindications, warnings and precautions, and common adverse reactions were identified in 31, 11, 77, and 46% of drugs, respectively. Five drugs (14%) were permanently or temporarily withdrawn from the US market. Drugs for hematological malignancies are often approved based on limited evidence through expedited regulatory pathways with incomplete safety profiles. Hematologists should be vigilant for unrecognized side effects when prescribing newly approved drugs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.273
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2019
Admission routes1
Has abstractyes

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