Comparison of 2 Serum Free Light Chain Assays with Creatinine Normal and Abnormal Populations Demonstrates the Need for Standardization
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to compare The Binding Site's Freelite on Optilite and Diazyme's Kappa/Lambda free light chains (K/L FLC) on Abbott Architect c8000 with healthy and renal insufficient populations and to evaluate their respective reference intervals for serum free light chains (sFLCs). METHODS: Two hundred sixty serum samples were measured for creatinine and sFLCs by both assays and a subset by immunofixation electrophoresis. Verification of manufacturer-defined reference intervals was assessed. RESULTS: Kappa free light chains (KFLC) showed excellent correlation of 0.998 R2 with a slope of 0.73. For Lambda free light chains (LFLC), an acceptable correlation of 0.953 R2 was found with a slope of 1.50 as well as a skewness-based difference with a -12.70 intercept. Healthy estimated glomerular filtration rate (eGFR) ≥60 reference interval verification of central 95% could not be confirmed for either Freelite or Diazyme although LFLC was much closer than KFLC for both assays with Freelite KFLC recovering only 37% of values within reference interval claims. The K/L FLC ratio did not meet 100% claim for both Freelite (91%) and Diazyme (95%) among those with eGFR ≥60. Samples with eGFR ≤59 had increasingly higher levels of KFLC and LFLC for both assays. When comparing worsening eGFR status, Freelite recovered increasingly higher ratios while Diazyme recovered increasingly lower ratios. CONCLUSIONS: Healthy reference intervals could not be verified for either Freelite or Diazyme. Renal reference intervals for Freelite are currently warranted while they are not recommended for Diazyme. The differences between these 2 assays can be minimized by standardization efforts such as recalibration.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".