Method choice for investigation of macrotroponin interference with the Siemens Atellica high sensitivity troponin I assay
Bibliographic record
Abstract
OBJECTIVES: Macrotroponin refers to circulating immunoglobulin-bound cardiac troponin species that may elevate troponin results in patients with or without myocardial injury, causing diagnostic confusion. Clinical laboratories have been recommended to provide a service for troponin interference investigation. We evaluated the applicability of a Protein A/G IgG-depletion procedure as well as polyethylene glycol (PEG) precipitation for detecting macrotroponin interference with the Siemens Atellica troponin I (TnIH) assay. METHODS: Troponin I, IgG, and albumin (internal standard) were measured (Atellica) on the neat and treated plasma to calculate recovery. Reference samples with TnIH ranging from < 1x to > 1000x times the 99th percentile were selected to verify expected recovery. To minimize likelihood of macrotroponin in the reference group, samples with elevated results were only included if recent acute changes in TnIH was documented. 40 samples were used for the IgG-depletion method and 20 for PEG precipitation. 25 samples from patients with unexplained elevation in TnIH were assessed by IgG-depletion. RESULTS: 38 of 40 reference group recoveries exceeded 70 % (median 91 %, IQR 15 %, max 129 %) in the IgG-depletion group consistent with literature on other assays. Specimens from patients with incongruent clinical picture had IgG-depletion recovery median of 11 % (IQR 14 %, max 37 %). PEG-precipitation showed large variation (median 103 %, IQR 89 %, max 227 %). CONCLUSIONS: IgG depletion using Protein A/G can reliably establish IgG-mediated interference with Atellica TnIH. PEG precipitation results are difficult to interpret likely due to matrix effects, especially at values closer to the 99th percentile.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".