Serum Free Light Chains in Neoplastic Monoclonal Gammopathies: Relative Under-Detection of Lambda Dominant Kappa/Lambda Ratio, and Underproduction of Free Lambda Light Chains, as Compared to Kappa Light Chains, in Patients With Neoplastic Monoclonal Gammopathies
Bibliographic record
Abstract
BACKGROUND: Quantitative evaluation of serum free light chains is recommended for the work up of monoclonal gammopathies. Immunoglobulin light chains are generally produced in excess of heavy chains. In patients with monoclonal gammopathy, κ/λ ratio is abnormal less frequently with lambda chain lesions. This study was undertaken to ascertain if the levels of overproduction of the two light chain types and their detection rates are different in patients with neoplastic monoclonal gammopathies. METHODS: Results of serum protein electrophoresis (SPEP), serum protein immunofixation electrophoresis (SIFE), urine protein electrophoresis (UPEP), urine protein immunofixation electrophoresis (UIFE), and serum free light chain assay (SFLCA) in patients with monoclonal gammopathies were examined retrospectively. RESULTS: The κ/λ ratios were appropriately abnormal more often in kappa chain lesions. Ratios of κ/λ were normal in about 25% of patients with lambda chain lesions in whom free homogenous lambda light chains were detectable in urine. An illustrative case suggests underproduction of free lambda light chains, in some instances. CONCLUSIONS: The lower prevalence of lambda dominant κ/λ ratio in lesions with lambda light chains is estimated to be due to relative under-detection of lambda dominant κ/λ ratio in about 25% of the patients and because lambda chains are not produced in as much excess of heavy chains as are kappa chains, in about 5% of the patients. The results question the medical necessity and clinical usefulness of the serum free light chain assay. UPEP/UIFE is under-utilized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".