Determining the adequate number of internal quality control levels: the example of coagulation factor VIII assay
Bibliographic record
Abstract
Taking the specific case of coagulation factor VIII assay, we determined the characteristics of an internal quality control panel assuring control of the assay method for all of the critical factor VIII concentrations. The precision of the assay method was determined on six control materials C1-C6, with expected factor VIII levels of 1, 5, 30, 50, 80 and 150 U/dl, respectively. Given that, when two control levels correlate statistically, the information provided by one of them is redundant, we used correlation and principal components analysis to define a priori adequate and inadequate control panels. For each of these panels, we calculated the number of runs required, using Hotelling's method, to detect a shift expressed on C1 and impacted on C2, C3, C4, C5 and C6 in relation to the correlation phenomena among the six levels. The C1/C6 panel proved to be as informative in this regard as the complete panel for a 1 U/dl shift simulated on C1 and impacted on other levels too. These correlation phenomena allow the biologist to implement fewer control levels than there are critical concentrations needing to be explored in the internal quality control plan.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".