Retrospective study of quantitative free light chain levels in random urine of patients with multiple myeloma
Bibliographic record
Abstract
The traditional method to quantify the immunoglobulin free light chains (FLCs) in patients with multiple myeloma (MM)is based on either their serum levels or on 24-hour urine collections. The latter method is cumbersome and ofteninaccurate, and serum levels are currently the preferred method for quantifying FLCs in MM. Scarce data exist on the FLCquantification in random urine samples. In this study, we first compared serum and urine levels of FLCs measured in24-hour specimens obtained from 56 consecutive MM patients. We subsequently examined the same correlation in 209random urine specimens obtained from a second cohort of 117 consecutive MM patients. Serum FLCs were highlycorrelated, both with the 24-hour and the random urine specimens. With the latter, the Pearson coefficient r was 0.805 and0.748 for kappa and lambda light chains, respectively (p<0.001), even in the presence of renal insufficiency. Random urineFLCs levels >1.2 mg/dL predicted a positive urine immunofixation with a specificity and sensitivity of 52.7% and 95.8%,respectively. Since random urine quantification of kappa and lambda FLCs paralleled their serum counterparts over time,this method could represent an additional non-invasive test for assessing disease activity in MM patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".