Improving quality of transcutaneous bilirubin measurements: Value of in-house developed quality control
Bibliographic record
Abstract
OBJECTIVES: Quality assurance (QA) plays an integral role in Point of Care Testing (POCT) programs. Quality control (QC) is an important QA program component to ensure high quality results and enhanced patient care. The measurement of transcutaneous bilirubin (TcB) in the POCT setting is an essential part of newborn care in Alberta, Canada. However, there is currently no available commercial QC material for TcB meters. An in-house developed QC material has been in use within a single TcB POCT program within Alberta. The objective of this study was to determine the performance of this QC material by other POCT staff and clinical end-users to assess whether its use could be expanded. DESIGN AND METHODS: Two levels of QC material were measured by POCT staff and clinical end-users across 12 different sites using the Dräger Jaundice Meter JM-103® and JM-105® meters. RESULTS: The performance of the QC material was acceptable when tested by POCT staff, was stable and reliable over time, and had an acceptable CV (≤8%). However, the data does not support use of the QC material by clinical end-users. CONCLUSIONS: The use of the QC material could be expanded into other TcB settings for use by POCT staff. Additional training and experience with the QC material by end-users is needed to facilitate QC use in the clinical setting.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 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".