Identifying sources of error and selecting quality indicators for point of care testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it