Quality in every drop: A national survey on calibration, control, and training in the use of pipettes in Norwegian medical laboratories
Bibliographic record
Abstract
Quality in every drop: A national survey on calibration, control, and training in the use of pipettes in Norwegian medical laboratories Introduction: Good practices for calibration, control, and training in pipette use are essential to reduce uncertainty in pipetting. The purpose of this study was to map how Norwegian medical laboratories ensure the quality of their pipettes. Materials and Methods: In January 2023, the Department of Medical Biochemistry and Pharmacology at Haukeland University Hospital, in collaboration with Noklus, conducted a national survey on pipette practices. The survey was distributed to 177 contacts at medical laboratories. Results: A total of 132 responses were received, of which 20 were excluded due to incomplete answers (yielding a response rate of 63%). Of the respondents, 84% reported regular calibration of pipettes. Among these, 36% perform internal calibration. Few of those who calibrate internally comply with all requirements in NS-EN ISO 8655 parts 2 and 6. Slightly more than half (about 59%) had established internal control procedures for pipettes. The most common approach is control with three volumes and ten measurements. Acceptance limits are mainly based on NS-EN ISO 8655. About half of the respondents stated that all employees receive training in pipette use. Conclusion: The study shows that Norwegian laboratories have varying practices for quality assurance of pipettes. Lack of compliance with international standards in internal pipette calibration and inconsistent training can weaken the quality of both pipettes and pipetting. Improved training and standardized internal calibration are necessary to ensure accurate pipetting and high analytical quality.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".