Identification methods as a factor affecting the performance of clinical microbiology laboratories participating in an external quality assessment program: a cross-sectional, retrospective analysis
Bibliographic record
Abstract
Introduction. Laboratory participation in external quality assessment (EQA) programmes including proficiency testing (PT) is a requirement of clinical laboratory conformance to ISO 15189:2022 Medical laboratories – Requirements for quality and competence . PT is one EQA method whereby laboratories are sent blinded samples for characterization by routine laboratory diagnostic methods. Importantly, PT enables a laboratory’s performance to be evaluated in comparison to the standard reference methods and to the performance of other peer laboratories using similar diagnostic methods. Gap statement. The desired outcome of participating in PT is to help laboratories identify possible sources of error in each step of the total testing process and particularly in their test methods during the analytical phase. Aim. This cross-sectional study investigated the impact of using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) compared to conventional phenotypic biochemical testing on laboratory performance in a clinical bacteriology PT scheme. Methodology. During a 6-year period from 2017-2022, the Canadian Microbiology Proficiency Testing implemented 112 PT challenges comprising 22 different sample types and included 61 different bacterial species. This was translated into 5883 graded test events for analysis. Multiple logistic regression techniques were employed to explore the association between the test method employed and laboratory performance. The sample type and aerobic classification of challenge organisms were included as confounding variables. Results. Laboratories using MALDI-TOF MS performed significantly better in characterizing microorganisms than laboratories using phenotypic biochemical testing alone [odds ratio OR = 5.68, confidence interval (CI): 3.92, 8.22] regardless of the sample type and aerobic classification. Notably, our analysis identified a significant association between anaerobic organisms and laboratory performance (OR: 0.24, CI: 0.17–0.35), suggesting that culturing and identifying fastidious organisms remains a significant obstacle for many clinical microbiology laboratories. Conclusions. Although no method is infallible and its performance will depend on the validation and quality assurance procedures, this finding may help the management in the decision for implementing MALDI-TOF MS in the microbiology laboratory. This study highlights the important role PT providers play in the objective assessment of laboratory performance and how it can provide evidence for quality improvement.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".