Bibliographic record
Abstract
Since the introduction of endoscopic inspection of joints in the 1960s, a few rheumatologists have sought to apply the technique to our patients. Surgical applications under arthroscopic guidance swept away simple arthroscopy in the 1970s1. Attempts to acquire full arthroscopic skills in the 1980s led rheumatologists at several institutions to enter operating rooms (OR) and apply arthroscopic techniques to a variety of clinical situations encountered in rheumatology, particularly arthroscopic debridement for knee osteoarthritis (OA) and major synovectomy for refractory knee synovitis in rheumatoid arthritis (RA)2. Advances in instrumentation that permitted arthroscopy to be performed in a procedure room or office setting fueled a surge of interest in the early 1990s, with highly popular instructional courses sponsored by the American College of Rheumatology (ACR) and private concerns. Arthroscopy study group meetings became a regular part of every national ACR yearly meeting. The nationwide burden of knee arthritis coupled with … Address correspondence to Dr. R.W. Ike, 300 N. Ingalls, SPC 5422, Ann Arbor, Michigan 48109-5422, USA. E-mail: rike{at}umich.edu, rike{at}med.umich.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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".