Bibliographic record
Abstract
These days every article requires disclosure of conflicts. And I have one for this editorial — I am a “physical examination” kind of doctor. Surely that is true of almost all rheumatologists because, despite the advances in ultrasound and magnetic resonance imaging, demonstration of the presence of inflammatory arthritis is still largely based on physical examination. And the usefulness of the physical examination is not limited to rheumatology. The techniques developed by René Laennec in part for his newly invented stethoscope (whispered pectoriloquy, egophony, and fremitus) along with the technique developed by Leopold Auenbrugger (percussion)1 are highly sensitive and specific for identifying lung infiltrates and effusions2. A seasoned examiner can diagnose aortic insufficiency more accurately than either M-mode or 2-dimensional echocardiogram3. Thus, the physical examination remains alive and well. Sjögren syndrome (SS) is a common problem, perhaps second only to rheumatoid arthritis among conditions that cause an inflammatory arthritis4. It can be considered an autoimmune epithelitis5, and predominately affects the exocrine organs. In fact, patients with primary SS can be conveniently divided … Address correspondence to Dr. H. Scofield, 825 NE 18th St., Oklahoma City, Oklahoma 73104, USA. E-mail: hal-scofield{at}omrf.ouhsc.edu.
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 | 0.013 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| 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".