Accre 8 emerging point of care CLIA system for vitamin B12 assessment compared with three established assays
Bibliographic record
Abstract
Accurate Vitamin B12 (Vit B12) quantification is essential for diagnosing deficiencies linked to neurological and hematological disorders. The Accre 8 Point-of-Care (POC) Chemiluminescent Immunoassay (CLIA) system offers a compact design, rapid single-step operation, and minimal calibration requirements. This study evaluates Accre 8's performance against established CLIA immunoassays (Abbott and Roche) and LC-MS/MS, the gold standard for Vit B12 quantification. A total of 297 serum samples, spanning deficient to sufficient Vit B12 levels, were analyzed. Accre 8 demonstrated a strong correlation with LC-MS/MS (r = 0.94, p < 0.001), with median Vit B12 levels closely aligning with LC-MS/MS (256.0 pmol/L). Accre 8 exhibited high sensitivity (96.9%) and specificity (86.7%), with Cohen's Kappa agreement (0.76). Bland-Altman analysis showed a mean bias of - 18.5%, while Passing-Bablok regression indicated proportional bias at higher concentrations (slope = 1.44). ROC analysis confirmed excellent diagnostic accuracy (AUC = 0.98). Accre 8's strong diagnostic performance, minimal calibration needs, and low sample volume requirements position it as a practical alternative to conventional CLIA systems for Vit B12 assessment, particularly in clinical and resource-limited settings. These findings support its potential integration into routine diagnostic workflows for Vit B12 deficiency screening and monitoring.
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 | 0.009 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".