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Record W2335904245 · doi:10.5737/236880762611928

Management of iron overload in the Canadian hematology/oncology population: Implications for nursing practice

2016· article· en· W2335904245 on OpenAlexaffvenueabout
Cindy Murray, Tammy De Gelder, Nancy Pringle, J. Colleen Johnston, Mary Doherty

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsToronto General HospitalJuravinski HospitalJuravinski Cancer CentrePrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineHematologyIntensive care medicineMalignancyOncology nursingInternal medicineHematological disordersHealth careNursingOncologyNurse education

Abstract

fetched live from OpenAlex

Red blood cell (RBC) transfusions are vital for many patients with chronic anemias associated with oncologic/hematologic disorders. However, repeated transfusions over time can lead to iron overload, which, if left untreated, can increase the risk of further malignancy and end-organ damage. Nurses and other health care professionals may not be aware of the significant implications of RBC transfusions and iron overload in patients with hematological/oncological disorders. This article was developed by a group of Canadian nurse practitioners and specialized oncology nurses to help improve health care professionals' understanding of iron overload in oncology patients and its associated risks, as well as provide a practical guide for the management of patients receiving treatment for this potentially serious condition.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.139
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.003
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.358
Teacher spread0.335 · 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 designNot applicable
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

Citations2
Published2016
Admission routes3
Has abstractyes

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Same venueCanadian Oncology Nursing JournalSame topicHemoglobinopathies and Related DisordersFrench-language works237,207