Quality Control Measurements Yield Similar Variation Below and Above Limit of Quantification for High-Sensitivity Cardiac Troponin I Assays
Bibliographic record
Abstract
There has been much focus on what constitutes an “undetectable” high-sensitivity cardiac troponin (hs-cTn) level. The International Federation of Clinical Chemistry Committee on Clinical Application of Cardiac Bio-Markers (IFCC C-CB) uses the limit of detection (LOD), in part, to aid in classifying an assay as a high-sensitivity assay. However, some regulatory bodies (i.e., United States Food and Drug Administration [FDA]) limit reporting of hs-cTn assays to values at or above the limit of quantification (LOQ) (1). How these metrics are derived varies between manufacturers; different approaches are proposed by different groups, with a 20% coefficient of variation (CV) often used to designate the LOQ (1). Assessing variability via %CV at low concentrations (i.e., <10 ng/L) may not capture acceptable variation, where absolute analytical variation of ±3 ng/L has been proposed as an analytical goal (2). Use of this analytical variability metric has support from different external proficiency testing schemes and has minimal impact (via simulation) on myocardial infarction diagnosis in the emergency setting (3, 4). For a ±3 ng/L analytical limit, a standard deviation (SD) of ≤1 ng/L would be suitable. However, this has not been formally evaluated at concentrations below the LOQ, especially with commercial quality control (QC) material. In the present analysis, we evaluated imprecision (SD and %CV) with a commercial QC material that yielded concentrations below and above the LOQ for different hs-cTn assays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".