Bibliographic record
Abstract
AIMS: We have tested the relative performance of 20 commercial ANA test kits along with that of our own laboratory to assess whether one was clearly superior. METHODS: The sera were drawn from 3 pools that had all been pre-tested in our laboratory: patients with definite SLE; patients with non-connective tissue diseases (CTD), but where a positive FANA had been found; and normal blood donors. The tests were used in accordance with the recommendations of the specific supplier but in a routine serology laboratory. RESULTS: Sensitivity and specificity ranged between 38 and 100%. While the negative predictive value of 4 ELISA kits was 100%, and most others were close, the HEp-2 kits were 100% in only 1 case. A positive predictive value of 100% was also seen with 1 kit. CONCLUSION: Some of the tests are clearly better than others, but the choice may differ depending on the clinical needs, e.g. preference for a good positive or negative predictive value. However, the ELISA kits offered better results than the immunofluorescent technique. Two of them had sensitivity/specificity of > 90%.
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 | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".