Screening for signs and symptoms of rheumatoid arthritis by family physicians and nurse practitioners using the Gait, Arms, Legs, and Spine musculoskeletal examination
Bibliographic record
Abstract
OBJECTIVE: To evaluate the sensitivity and specificity of the Gait, Arms, Legs, and Spine (GALS) examination to screen for signs and symptoms of rheumatoid arthritis (RA) when used by family physicians and nurse practitioners. METHODS: Participating health care professionals (2 rheumatologists, 3 family physicians, and 3 nurse practitioners) were trained to perform the GALS examination by viewing an instructional DVD and attending a training workshop. One week after training, the health care professionals performed the GALS examination on 20 individuals with RA and 21 individuals without RA. All participants were recruited through 2 rheumatology practices, and each participant was assessed by 4 health care professionals. The health care professionals were asked to record whether observed signs and symptoms were potentially consistent with a diagnosis of RA. The health care professionals understood the study objective to be their agreement on GALS findings among one another and were unaware that one-half of the participants had RA. Sensitivity and specificity were calculated to determine the ability of the GALS examination to screen for RA using the rheumatologist as the standard for comparison. RESULTS: Sensitivity and specificity values varied from 60-100% and 70-82%, respectively, for the 3 family physicians, and 60-90% and 73-100%, respectively, for the 3 nurse practitioners. CONCLUSION: Following a very short training period, family physicians and nurse practitioners appeared to be able to use the GALS examination as a screening tool for RA signs and symptoms, particularly for identifying an individual with positive results who will benefit from further investigation or rheumatology referral.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".