Bibliographic record
Abstract
Serum free light chain assays have long been known to have a variety of analytical issues. These issues include excessive lot-to-lot variability, nonlinearity, hook effect, and overrecovery. Depending on the vendor, reagents are produced as either polyclonal antibodies by immunizing sheep or monoclonal antibodies generated by hybridomas. Polyclonal antibodies rely on a mixture of free light chains for immunization, whereas monoclonal antibodies are specifically targeted toward the constant portion (CL domains) of the light chain. In either case, consistent calibration and definition of a reference standard is defied by the variability in the serum free light chain target in patients (1). Free light chains can polymerize, thus forming large immunoreactive complexes. These multireactive polymers can cause overestimation of serum free light chain concentration through excess light scattering. Gel filtration studies have identified aggregates, which have been attributed as the cause of gross overestimation of light chain concentration, at times in vast excess of the total protein concentration.
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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.132 | 0.067 |
| Insufficient payload (model declined to judge) | 0.039 | 0.029 |
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".