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The Use of Erythropoietic Agents in Patients with Non-Myeloid Hematological Malignancies.

2006· article· en· W2592679594 on OpenAlexaffabout
Nadine Shehata, Irwin Walker, Ralph M. Meyer, Adam E. Haynes, Kevin Imrie

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsSunnybrook Health Science CentreMcMaster UniversityCancer Care OntarioQueen's UniversityMcMaster University Medical CentreOntario Institute for Cancer ResearchCanadian Blood ServicesUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineGuidelineRandomized controlled trialMEDLINECochrane LibraryHematocritClinical trialIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Practice guidelines for use of an erythropoietic agent (EpA) have been previously developed for patients with all cancers, but these have not specifically addressed non-myeloid hematological malignancies. Given issues of benefit, cost, access to treatment and practice variation for these patients, the Cancer Care Ontario Program in Evidence - Based Care (CCO PEBC) conducted a systematic review and has developed a practice guideline. Entries to MEDLINE, CANCERLIT, EMBASE, the Cochrane Library databases, and abstracts of the American Societies of Clinical Oncology (ASCO) and Hematology (ASH) were searched. A hierarchy of outcomes was developed that included survival, quality of life (QoL), transfusion requirements and improvements in hemoglobin concentration/hematocrit. To validate conclusions and recommendations, the practice guideline was sent to Ontario practitioners in July 2006; responses are now being collated. Eighteen reports (14 articles, 4 abstracts) of randomized trials met eligibility criteria. None of the 3 trials reporting survival outcomes detected a benefit with use of an EpA. Of 7 trials evaluating QoL, 6 reported superior outcomes in patients receiving an EpA. None of those trials sufficiently reported methodologic parameters required by proposed guidelines for analyzing, interpreting and reporting QoL measures. Use of an EpA significantly reduced the proportion of patients transfused in 5 trials evaluating this outcome; reductions ranged from 15% to 40%. The absolute risk reduction ranged from 15% to 24%; the number needed to treat to prevent a transfusion ranged from 4 to 6. Use of an EpA was not associated with a reduction in the mean/median number of units transfused. In 10 studies, either a statistically significant increase in the hemoglobin concentration/hematocrit or in the proportion of patients with an increase in the hemoglobin concentration/hematocrit was seen. For this practice guideline, we interpreted the evidence as providing insufficient data to justify use of an EpA in order to improve survival or QoL. However, there are compelling data showing that use of an EpA reduces the risk of requiring a transfusion. Initiatives such as the Commission of Inquiry on the Blood System in Canada (“the Krever Commission”), state that alternatives to transfusion be offered to patients because of associated known and potentially unknown adverse consequences of blood products. Thus, the use of an EpA is recommended as an alternative to transfusion. However, the decision to use an EpA to reduce transfusion requirements should primarily consider individual patient values and the likelihood that a patient will require a transfusion so that patients are not exposed to unnecessary treatment and the health care system to additional costs.

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)0.012
metaresearch head score (Gemma)0.056
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.233
Teacher spread0.213 · 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

Citations1
Published2006
Admission routes2
Has abstractyes

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