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Record W2105633504 · doi:10.12927/hcq.2005.17668

Using Healthcare Failure Mode and Effect Analysis Tool to Review the Process of Ordering and Administrating Potassium Chloride and Potassium Phosphate

2005· article· en· W2105633504 on OpenAlexaffabout
Rosmin Esmail, Cheryl Cummings, Deonne Dersch, Greg Duchscherer, Judy Glowa, Gail Liggett, Terrance Hulme

Bibliographic record

VenueHealthcare Quarterly · 2005
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsPrioritizationRenal replacement therapyMedicineHealth careIntensive care unitPotassiumMultidisciplinary approachIntensive care medicinePatient safetyAdverse effectEmergency medicineMedical emergencyInternal medicineBusinessChemistry

Abstract

fetched live from OpenAlex

During the spring of 2004, in the Calgary Health Region (CHR) two critical incidents occurred involving patients receiving continuous renal replacement therapy (CRRT) in the intensive care unit (ICU). The outcome of these events resulted in the sudden death of both patients. The Department of Critical Care Medicine's Patient Safety and Adverse Events Team (PSAT), utilized the Healthcare Failure Mode and Effect Analysis (HFMEA) tool to review the process and conditions surrounding the ordering and administration of potassium chloride (KCI) and potassium phosphate (KPO4) in our ICUs. The HFMEA tool and the multidisciplinary team structure provided a solid framework for systematic analysis and prioritization of areas for improvement regarding the use of intravenous, high-concentration KCL and KPO4 in the ICU.

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)0.150
metaresearch head score (Gemma)0.298
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.796

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1500.298
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.009
Bibliometrics0.0290.009
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.340
Teacher spread0.322 · 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 designQualitative
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

Citations76
Published2005
Admission routes2
Has abstractyes

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