A Survey of aPTT Reporting in Canadian Medical Laboratories
Bibliographic record
Abstract
A survey of all licensed medical laboratories performing activated partial thromboplastin time (aPTT) testing in Canada was undertaken; the response rate was 50.7%. Preanalytic phase of testing seemed generally satisfactory, although 46% of laboratories were still using 3.8% or a 129-mmol/L concentration of citrate, and only 59% of institutions routinely performed testing to verify the platelet-poor status of the plasma used for aPTT testing. There were also concerns relating to the speed and duration of centrifugation for specimen preparation. While more than 67% of institutions had established an individual therapeutic range for aPTT testing, only 47% of laboratories verified this range with heparinized samples. Approximately 67% of the institutions that had verified the range had done this by spiking heparin concentrations into pooled plasma rather than using ex vivo specimens from patients receiving heparin therapy. There seemed to be a need for increased education about circumstances under which the therapeutic range should be rechecked and current standards for screening for the lupus anticoagulant. More than 71% of Canadian institutions surveyed used low-molecular-weight heparin, which may obviate many of the issues surrounding aPTT testing. Overall performance as documented by survey results seemed similar to that reported for the United States and Australasia.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".