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Record W1969894871 · doi:10.1080/10428190310001615666

Subdural Hematomas during CML Therapy with Imatinib Mesylate

2004· article· en· W1969894871 on OpenAlexaff
K Song, Joshua Rifkind, Bassim Malas Al-Beirouti, K Yee, J McCrae, HA Messner, Armand Keating, JH Lipton

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2004
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsImatinib mesylateMesylateMedicineImatinibSubdural HematomasOncologyInternal medicineMyeloid leukemiaSurgeryHematomaChemistry

Abstract

fetched live from OpenAlex

Seven of one hundred twenty-one patients with chronic myeloid leukemia (CML) treated with imatinib mesylate developed subdural hematomas. All had advanced disease and were treated initially at a dose of 600 mg per day. Three patients had thrombocytopenia (platelet < 10 x 10(9)/l), one had leukocytosis (white blood cell count > 150 x 10(9)/l) and three had neither around the time of diagnosis of the subdural hematomas. Four patients required surgical evacuation. One patient, in blast crisis, died as a consequence of the subdural hematoma. Three patients survived but died of progressive CML. The remaining three patients having recommenced imatinib, are alive and well, and one has achieved a major cytogenetic response. Subdural hematomas must be considered even in mildly symptomatic patients receiving imatinib regardless of their peripheral blood counts. Patients who survive can be cautiously restarted on imatinib. Further studies are required to study the potential relationship between imatinib mesylate and subdural hematomas.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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 designCase report
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

Citations38
Published2004
Admission routes1
Has abstractyes

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