Internal Quality Control Practices in Coagulation Laboratories: recommendations based on a patterns‐of‐practice survey
Bibliographic record
Abstract
INTRODUCTION: Internal quality control (IQC) procedures are crucial for ensuring accurate patient test results. The IQMH Centre for Proficiency Testing conducted a web-based survey to gather information on the current IQC practices in coagulation testing. METHODS: A questionnaire was distributed to 174 Ontario laboratories licensed to perform prothrombin time (PT) and activated partial thromboplastin time (APTT). RESULTS: All laboratories reported using two levels of commercial QC (CQC); 12% incorporate pooled patient plasma into their IQC program; >68% run CQC at the beginning of each shift; 56% following maintenance, with reagent changes, during a shift, or with every repeat sample; 6% only run CQC at the beginning of the day and 25% when the instruments have been idle for a defined period of time. IQC run frequency was determined by manufacturer recommendations (71%) but also influenced by the stability of test (27%), clinical impact of an incorrect test result (25%), and sample's batch number (10%). IQC was monitored using preset limits based on standard deviation (66%), precision goals (46%), or allowable performance limits (36%). 95% use multirules. Failure actions include repeating the IQC (90%) and reporting patient results; if repeat passes, 42% perform repeat analysis of all patient samples from last acceptable IQC. CONCLUSION: Variability exists in coagulation IQC practices among Ontario clinical laboratories. The recommendations presented here would be useful in encouraging standardized IQC practices.
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How this classification was reachedexpand
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 | 0.010 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".