Bibliographic record
Abstract
Dr. Korkmaz raises the following issues: (1) the fluctuation of the aCL antibody titers, either spontaneous or modified by prednisone treatment, might be responsible for the upregulation of aPL antibodies after CYC treatment and not CYC itself; (2) his personal experience from 4 patients with SLE treated either with CYC orAZA alone or with prednisone is opposite to our own1; (3) the diagnosis of some patients with APS may not be accurate; and (4) renal biopsy findings of a patient with APS nephropathy are missing. Fluctuation of aCL antibody titers was observed in both treatment groups. However, high antibody titers (absorbance higher than the 99th percentile of 100 normal individuals in ELISA) obtained on 2 occasions 12 weeks apart were commonly detected in the CYC-treated group and constituted the criterion for seroconversion. Patients with high aCL titers tended to be permanently positive for aCL. The patients from both groups were evaluated for aCL antibodies with the same frequency over time. The … Address reprint requests to Dr. Vlachoyiannopoulos; E-mail: pvlah{at}med.uoa.gr
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".