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Record W4416354951 · doi:10.1515/cclm-2025-1462

Recommendations from the IFCC Working Group on Laboratory Errors and Patient Safety for the Global Adoption of an Essential Quality Indicators Panel in Laboratory Medicine

2025· article· en· W4416354951 on OpenAlexaff
Vincent De Guire, César Alex de Oliveira Galoro, Mercè Ibarz, Agnes Ivanov, Giuseppe Lippi, Rui Zhou, Laura Sciacovelli, Julie Shaw, Wilson Shcolnik, Ruben L. Smeets, Zorica Šumarac, Pieter Vermeersch, Alexander Meyer, Mario Plebani

Bibliographic record

VenueClinical Chemistry and Laboratory Medicine (CCLM) · 2025
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsCanadian Electricity AssociationOttawa HospitalUniversity of OttawaHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsPatient safetyMedical laboratoryQuality (philosophy)Panel discussionLaboratory safetyQuality managementNational laboratory

Abstract

fetched live from OpenAlex

OBJECTIVES: To maximize participation in the international standardization effort, this recommendation aims to update the international guidance of the Working Group on Laboratory Errors and Patient Safety of the International Federation of Clinical Chemistry and Laboratory Medicine by identifying a limited and globally applicable panel of Essential Quality Indicators (QIs) focused on patient safety, clinical outcomes, and harmonization across medical laboratories. METHODS: Through a consensus meeting, experts from multiple countries reviewed the IFCC Model of Quality Indicators (MQI) to identify high-priority indicators. Selection prioritized the probability of patient harm, ease of detection, and feasibility of data collection and implementation within national contexts. RESULTS: Six essential QIs covering the total testing process were identified and ratified: (1) rate of misidentified requests (Pre-MisR) and misidentified samples (Pre-MisS); (2) rate of sample rejections (Pre-RejS); (3) rate of hemolysis detected either by automated hemolysis index (Pre-HemI) or visual inspection (Pre-HemV); (4) rate of unacceptable results in External Quality Assessment/Proficiency Testing (Intra-Unac); (5) turnaround time of cardiac troponin at the 90th percentile for the emergency room (Post-TnTAT, Post-TnTAT clin); and (6) rate of incorrect laboratory reports (Post-RectRep). Recommendations on calculation, reporting frequency, and integration into IFCC and national comparison programs are provided. CONCLUSIONS: The proposed essential QI panel provides a standardized and feasible framework to support its integration into national comparison programs and the IFCC MQI platform. Its implementation will facilitate data consolidation, strengthen the development of national and global quality specifications, and contribute to continuous improvement in patient safety.

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)0.201
metaresearch head score (Gemma)0.264
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2010.264
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0040.012
Bibliometrics0.0160.010
Science and technology studies0.0050.007
Scholarly communication0.0120.010
Open science0.0150.010
Research integrity0.0320.032
Insufficient payload (model declined to judge)0.0080.008

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 head0.082
GPT teacher head0.424
Teacher spread0.342 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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