Method Precision and Frequent Causes of Errors Observed in Point-of-Care Glucose Testing
Bibliographic record
Abstract
OBJECTIVES: Method imprecision, error rates, and explanatory causes that were identified in the Institute for Quality Management in Healthcare point-of-care (POC) glucose proficiency testing (PT) program were assessed in comparison with results obtained from laboratory glucose PT surveys. METHODS: POC and laboratory glucose PT data were assessed from September 2009 to June 2011. Peer group means and coefficients of variation (CVs) were estimated using the robust algorithm recommended in the International Organization for Standardization/International Electrotechnical Commission 13528(E). Discordant finding investigations were also reviewed to determine the causes of significant and recurring errors. RESULTS: POC glucose CVs were higher than laboratory method CVs (median CV, 4.5% and 1.6%, respectively). While all laboratory glucose results were within the performance limits, 305 (0.59%) of 51,379 POC glucose results exceeded limits. Investigations were required for 277 (0.53%) POC results. Pre- and postanalytical errors accounted for 76% of the discordant findings. Using wrong PT items, sample mix-up on the bench, and reporting results for the wrong sample were the most frequent reasons, while 21% of discordant findings identified manufacturer issues, and 3% were of unknown origin. CONCLUSIONS: Both method CVs and error rates were higher in POC than in laboratory glucose methods, even though larger performance limits were used for the assessment of POC glucose.
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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 | 0.073 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".