501 The number of patient errors reported per year is a superior risk metric compared to sigma
Bibliographic record
Abstract
Abstract Introduction/Objective Sigma metrics, calculated as [(TEa - |bias|)/SD], are frequently used to estimate errors per million results in analytical processes. Problems relative to risk management include: 1. Sigma measures proximity to the nearest allowable error limit but underestimates errors when bias is minimal or absent. 2. Sigma does not vary with the number of patient samples analyzed annually. Methods/Case Report We calculated sigma for 224 routine chemistry QC samples from four instruments across three laboratories based on January 2025 data. We also used software from ElevateQC (Toronto, Ontario, Canada) to calculate sigma metrics and the number of results outside both upper and lower TEa limits annually based on patient volumes. Results QC samples represented between 3,650 and 30,660 patient results annually. In one case, a 14% increase in sigma (0.83 to 0.95) led to a 724% increase in annual errors (729 to 5,279.) Similarly, a 23% increase in sigma (0.83 to 1.02) resulted in a 425% increase in errors (729 to 3,099). The correlation coefficient between sigma and errors per year was only 0.18. Conclusion Errors per year is a more informative metric than sigma, especially when communicating patient risk levels to non-scientists.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".