The Dorsal 4-finger Technique: A Novel Method to Examine Metacarpophalangeal Joints in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To describe the dorsal 4-finger technique (DFFT) in examining metacarpophalangeal (MCP) joints of patients with rheumatoid arthritis (RA) and compare it to the traditional 2-finger technique (TFT) using ultrasound (US) as a gold standard. METHODS: Four rheumatologists evaluated 180 MCP joints of 18 patients with RA. All patients underwent US for greyscale (GSUS) and power Doppler US (PDUS). Agreements between rheumatologists, the 2 techniques, and US were evaluated using Cohen κ and the first-order agreement coefficient (AC1) κ methods. RESULTS: The population comprised 17 females (94.4%) with a mean (SD) age and disease duration of 56.8 (14.4) and 21.8 (12.9) years, respectively. Eight patients (44.4%) were taking methotrexate monotherapy, while 10 patients (55.6%) were receiving biologics. US evaluation revealed 69 (38.3%) and 30 (16.7%) joints exhibited synovitis grade 2-3 by GSUS and PDUS, respectively. Effusion was documented in 30 joints (16.7%). The mean intraobserver agreement using the DFFT and TFT were 80.5% and 86%, respectively. The mean interobserver agreements using the DFFT and TFT were 84% and 74%, respectively. κ agreement with US findings was similar for both techniques in tender joints but was higher for the DFFT in nontender joints (0.33 vs 0.07, p = 0.015 for GSUS) and (0.48 vs 0.11, p = 0.002 for PDUS). The DFFT had a higher sensitivity in detecting ballottement by GSUS (0.47 vs 0.2, p < 0.001) and PDUS (0.60 vs 0.27, p < 0.001). CONCLUSION: The DFFT is a novel, reproducible, and reliable method to examine MCP joints, and it has a better correlation with US than the traditional TFT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".