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Record W2809659752 · doi:10.4212/cjhp.v50i1.2003

Erythropoietin Use in a Hemodialysis Population

2018· article· en· W2809659752 on OpenAlexvenueno aff
Diane Chong, Gordon Lane, Nancy M. Waite

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2018
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsErythropoietinMedicineAnemiaHemodialysisHemoglobinHematocritPopulationDialysisTarget rangeFerritinSurgeryInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT The purpose of this project was to assess how well erythropoietin improves anemia in a hemodialysis population, and if anemia was not resolved, to determine the contributing factors, and to assess the frequency and severity of complications of erythropoietin therapy. DUE criteria were developed based on published literature. Hemodialysis patients initiating erythropoietin therapy were entered in a retrospective chart review. Data were collected from the one month prior to erythropoietin therapy to six months after initiation of erythropoietin. During the six-month period, 54.3% of patients reached the target hemoglobin range of 105-115 g/L, however, at the conclusion of the period, only 32.6% were in the target range. Six months after the initiation of erythropoietin therapy, 78.6% of patients required fewer blood transfusions compared to a six-month baseline period. Seventy eight percent of patients with ferritin concentrations <100 ng/mL received iron supplementation. Eighty-three percent of erythropoietin dose changes were appropriate. Hemoglobin and hematocrit were monitored on average once every two weeks. An elevated diastolic blood pressure was detected in 63% of patients. No seizures or allergic reactions were noted. Our dialysis patients did not receive maximum benefit from erythropoietin. Contributing factors include low initial and maintenance doses, inadequate monitoring of hemoglobin and iron therapy, and inaccurate dose modifications. It is recommended that an algorithm be developed for closing and monitoring of erythropoietin.  RESUME  L'objet de ce projet etait d'evaluer dans quelle mesure l'erythropoietine corrige l'anemie chez les hemodialyses. Si l'anemie n'etait pas corrigee, on determinait les facteurs qui ont contribue a cet etat et on evaluait la frequence et la gravite des  complications du traitement a l'erythropoietine. Des criteres EUM ont ete elabores a partir De la documentation existante. Une analyse retrospective des dossiers medicaux des patients hemodialyses qui amorcaient un traitement a l'erythropoietine a ete menee. Les donnees ont ete recueillies dans le mois precedent le debut du traitement a l'erythropoietine et durant les six mois suivants. Au cours de la periode therapeutique de six mois, 54,3 % des patients ont atteint des taux d'hemoglobine cibles variant entre 105 et 115 g/L; cependant ce taux a chute a seulement 32,6 % au terme du projet. Six mois apres le debut du traitement a l'erythropoietine, 78,6 % des patients ont eu besoin d'un moins grand nombre de transfusions, comparativement a Ia periode initiale. De plus, 78 % des patients dont les concentrations de ferritine etaient inferieures a 100 ng/mL ont recu un supplement de fer. Les modifications posologiques de l'erythropoietine etaient appropriees dans 83 % des cas. Les taux d'hemoglobine et d'hematocrites etaient mesures en moyenne a toutes les deux semaines. On a observe une tension diastolique elevee chez 63 % des patients, mais aucune crise ni reaction allergique. Les patients hemodialyses n'ont pas tire un avantage maximum de l'erythropoietine. Les facteurs ayant contribue a ce phenomene comprennent des doses initiales et d'entretien faibles, une surveillance inadequate des taux d'hemoglobine et du traitement suppletif enfer, et une modification posologique inadequate. On recommande de mettre au point un algorithme des modalites posologiques et de surveillance du traitement a l'erythropoietine.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.027
GPT teacher head0.297
Teacher spread0.270 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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