Selection of the Primary Quality Control Rules Based on Total Allowable Error and Total Error (by Hand or Laptop)
Bibliographic record
Abstract
ABSTRACT Choosing quality control (QC) rules for monitoring quantitative methods is compulsory, often frustrating, and not easy. As part of the protocol, there are many possible QC statistical “rules” (eg, rejecting a single value outside 2 SDs) to be selected. Each analyte should use the rule or rules that have the fewest accepted wrong results (for patients and controls). Selecting the best primary QC rule ensures the development of a simple, rapid system that calculates the rule best primary for each level used for an analyte. The algorithm uses 3 readily available data points for each QC level—the laboratory’s mean, SD, and the true (survey) mean. With these data and the total error allowable (TEa), the program calculates the values for the total error (TE) and Tea − TE. This algorithm generates the primary QC rule (eg, 12 SD, 2.5 SD, 13 SD rule). The rules, −12.5 or 13 SDs (and ones in between if wanted), will reduce wrong results without accepting false results. Additionally, QC rules such as 41 SD and 10 SD are no longer necessary. The 22-SD rule need not be rejected, but the user need only be aware. Using the algorithm by hand or laptop is easy and removes the guesswork of choosing the primary QC rules.
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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 | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.028 |
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".