Identifying sources of error and selecting quality indicators for point of care testing
Bibliographic record
Abstract
OBJECTIVES: Point of Care Testing (POCT) is a rapidly expanding area of clinical laboratory testing and quality assurance is an important area of focus. Quality indicators (QIs) are a quality management system tool that monitors aspects of the testing process to help meet the challenges associated with maintaining high quality patient safety given the growth in POCT. Alberta aims to formalize the development and use of QIs for POCT. DESIGN: and Methods: Potential QIs were identified by reviewing both the current standards and guidelines for QIs in POCT, and the research regarding quality and sources of error in POCT. Quality practices and potential sources of error in POCT were identified by: 1) a Canadian national survey on POCT, and 2) direct observation in two local POCT programs. RESULTS: A proposed selection of QIs in POCT were identified by incorporating the results from these investigations, while considering the unique characteristics of POCT. These QIs monitor the preanalytical, analytical, and post-analytical phases of testing, and support processes. CONCLUSIONS: As POCT volumes and test menu expands, QIs will be a vital tool in monitoring error and maintaining high quality of results. Adoption of formal QIs will support continuous quality improvement and improved patient care.
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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 | 0.079 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".