Bibliographic record
Abstract
E. Smith has pointed out that the use of cryocrit in the investigation of cryoglobulinemia has analytical and clinical limitations, an observation with which we concur in general. The emphasis of our case study, however, was not to make a judgment on which technique should be used, but rather to focus on the importance of including cryoglobulin analysis in the differential and to address the need for standardization between laboratories. We reported a schema of sample collection and analysis that merely reflects a practice that many laboratories still conduct today. Vermeersch et al. reported that up to 37% of the 140 surveyed laboratories included cryocrit or other estimates (e.g., total protein) in their patient reports (1). The lack of standardization of practice appears, at least in part, to be related to a difference in opinion on the usefulness of estimating cryoglobulin quantities. Although some investigators reported no relationship between cryoglobulin concentration and the severity of symptoms and disease activity(2), cryoglobulin concentrations have, nevertheless, been found to correlate with response to treatment with plasmapheresis, cytotoxic agents, and/or interferon α(3). Moreover, we did indicate in our report that cryocrit does not differentiate type 1 and type 2 cryoglobulinemia, and we recommended that serum protein electrophoresis and immunofixation should be conducted on resolubilized cryoprecipitate(4). Certainly, laboratories that do not have specialized equipment or a test in place to screen for cryoglobulins should consider at minimum estimating the cryocrit.
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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.041 | 0.041 |
| Insufficient payload (model declined to judge) | 0.026 | 0.016 |
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".