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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.201 | 0.264 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.015 | 0.010 |
| Research integrity | 0.032 | 0.032 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".