Bibliographic record
Abstract
Joint lavage aims to remove debris such as microscopic or macroscopic fragments of cartilage matrix, bone macromolecules, and crystals that may induce synovitis, a likely source of pain and a putative cause of chondrolysis.1 Joint lavage has been used for several decades by rheumatologists and orthopedists for the treatment of knee osteoarthritis (OA) and septic arthritis. It can be performed during an arthroscopy, or more easily, on its own by using 1 or 2 needles, allowing the injection of saline (1-3 L) into the joint cavity, which is then evacuated. In this case, the procedure is easy and less expensive, and depending on the disease, can be followed by an intraarticular (IA) injection of corticosteroids. In this issue of The Journal of Rheumatology , Drs. Ike and Kalunian have written a review of the literature on the efficacy of joint lavage for different conditions, not only for knee OA but also for inflammatory arthropathies, microcrystalline arthritis, and septic arthritis.2 This is not a systematic literature review (SLR), but a narrative review. The authors suggest that there is still a place for joint lavage in patients with knee OA.2 However, 2 SLRs conducted on this subject concluded that lavage did not provide any clinically relevant benefit in … Address correspondence to Dr. P. Richette, Hôpital Lariboisière, Rheumatology, 2, rue A. Pare, Paris 75010, France. Email: pascal.richette{at}aphp.fr.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".