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Record W2945425711 · doi:10.1080/10428194.2019.1613539

Management of cardiovascular health in acute leukemia: a national survey

2019· article· en· W2945425711 on OpenAlexaffabout
Michelle Durand, Katie Lacaria, M. Sidsworth, Margot K. Davis, David Sanford

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2019
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsLeukemia & Lymphoma Society of CanadaVancouver General Hospital
Fundersnot available
KeywordsMedicineCardiotoxicityComorbidityIntensive care medicineInternal medicineDiseaseHealth careHematologyMultidisciplinary approachAnthracyclineChemotherapyCancer

Abstract

fetched live from OpenAlex

Cardiovascular (CV) disease is a common comorbidity in acute leukemia (AL) patients and can be worsened by the use of anthracyclines. Interruptions and underutilization of CV medications during AL treatments may negatively impact the CV health of these patients. A 30-question electronic survey was distributed to Canadian hematologists who treat adults with AL to determine the frequency, timing and rationale for interruptions in statins, antiplatelets and angiotensin antagonists in patients undergoing intensive chemotherapy. Strategies for mitigating anthracycline cardiotoxicity, methods of establishing baseline CV risk and utilization of clinical pharmacists were also assessed. Results indicate that it is common for AL patients undergoing intensive chemotherapy to require CV medication interruptions. This highlights the need for collaboration between hematology and cardiology healthcare teams and utilization of multidisciplinary healthcare professionals to improve CV care during AL.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.262
Teacher spread0.246 · 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 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 routes2
Has abstractyes

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