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Record W4212942000 · doi:10.1093/jcag/gwab049.028

A29 ALBUMIN UTILISATION AT A TERTIARY CARE HOSPITAL

2022· article· en· W4212942000 on OpenAlexaffabout
Abacha Mohammed, D Goldshtein, S Sarbar, Sarah Wong, Jennifer A. Flemming

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineAlbuminMedical prescriptionIntensive care unitIntensive care medicineCirrhosisEmergency medicineInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Abstract Background Albumin is a colloidal solution with usage guided by recommendations from Canadian Blood Services (CBS). In Ontario, rates of albumin use increased substantially from 2012 to 2018 despite the lack of development of new indications for using over this time frame. Further, we recently found that >50% of albumin usage in patients with cirrhosis were non-evidence based. Albumin usage in other patient populations is not well described. Aims Our aim was to describe overall usage of albumin stratified by clinical setting, clinical indication, and dosage at a tertiary care hospital. Methods We retrospectively identified all albumin prescriptions during two randomly selected non-consecutive months between 2018 and 2019 at Kingston Health Sciences Centre. Data was abstracted from each hospital chart for indication, prescriber specialty, location of patient, amount of albumin ordered, and concentration (5% or 25%). Albumin prescriptions were then defined as either evidence-based or non-evidence based on published literature and guidelines. Overall cost for albumin during the study period was determined based on CBS pricing ($51.47/ 25gms). Results A total of 699 albumin prescriptions were dispensed to 317 individuals over December 2018 and May 2019, with a total of 38,458 grams used. Overall, 36% was prescribed for evidence-based indications. The most common indication was plasmapheresis (32%), non-sepsis volume resuscitation (23%), and cardiac surgery (17%). The majority of albumin was prescribed in dialysis unit (32%), ICU (23%), and cardiac sciences unit (11%). The largest prescribers of albumin were intensivists (26%), followed by nephrologists (17%), and cardiac surgeons (15%). There were differences in utilization based on concentration of albumin. Despite limited evidence of benefit, 25% albumin and 5% albumin were used excessively for volume resuscitation and cardiac surgery respectively. When used for an evidence-based indication, the dosing was incorrect in 45% of orders. A total of $79,176 was spent on albumin during the study period. Importantly, only $28,494 was spent on evidence-based indications with appropriate dosage and concentration. Conclusions Overall, there is significant albumin use for indications lacking substantial evidence. This study identifies the clinical contexts in which there is opportunity to reduce non-evidence-based albumin usage and cut unnecessary expenditure. Targeted quality improvement initiatives are underway. Funding Agencies None

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 score0.116
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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