Appropriateness of laboratory tests in the diagnosis of inflammatory rheumatic diseases among patients newly referred to rheumatologists
Bibliographic record
Abstract
INTRODUCTION: Autoantibody tests are commonly ordered when screening for rheumatic diseases. Rheumatoid factor (RF) and antinuclear antibody (ANA) have low positive predictive values in general practice. Overuse of diagnostic tests can result in an increase in unnecessary referrals, patient anxiety, and further costs. OBJECTIVE: The objective was to evaluate the utilization patterns, appropriateness, and associated costs of tests including ANA, extractable nuclear antibodies (ENA), anti-double stranded DNA (anti-dsDNA), RF, and HLA-B27 in patients referred to rheumatologists. METHODS: A review was conducted of consecutive referrals (accepted and rejected) using university rheumatologists' practices over one year. Inappropriate investigations, and associated costs were analyzed. Tests were considered appropriate if at least one criterion for a specific disease was provided. RESULTS: Of 638 referrals the most common reported reasons for referral were: spondyloarthropathies (SpA), rheumatoid arthritis (RA), and lupus (SLE). Prior to referral: 61% had undergone ANA testing at least once, ANA was repeated in one third; 19% had ENA and 21% had anti-dsDNA. 20% had ANA testing with no clinical indication. Half of ENA and anti-dsDNA testing was in the context of a negative ANA. RF was requested in 65% and in close to one third, there was no clinical suspicion of inflammatory arthritis. CONCLUSION: Despite the recommendations by CRA Choosing Wisely Campaign, at least 50% of laboratory investigations, including RF, ANA, ENA, and anti-dsDNA, are inappropriately ordered. More selective ordering of the above tests would lead to marked cost reduction.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".