Algorithm for Identification of Undifferentiated Peripheral Inflammatory Arthritis: A Multinational Collaboration Through the 3e Initiative
Bibliographic record
Abstract
OBJECTIVE: To develop an algorithm for identification of undifferentiated peripheral inflammatory arthritis (UPIA). METHODS: An algorithm for identification of UPIA was developed by consensus during a roundtable meeting with an expert panel. It was informed by systematic reviews of the literature used to generate 10 recommendations for the investigation and followup of UPIA through the 3e initiative. The final recommendations from the 3e UPIA Initiative were made available to the panel to guide development of the algorithm. The algorithm drew on the clinical experience of the consensus panel and evidence from the literature where available. RESULTS: In patients presenting with joint swelling a thorough evaluation is required prior to diagnosing UPIA. After excluding trauma, the differential diagnosis should be formulated based on history and physical examination. A minimum set of investigations is suggested for all patients, with additional ones dependent on the most probable differential diagnoses. The diagnosis of UPIA can be made if, following these evaluations, a more specific diagnosis is not reached. Once a diagnosis of UPIA is established, patients should be closely followed as they may progress to a specific diagnosis, remit, or persist as UPIA, and additional investigations may be required over time. CONCLUSION: Our algorithm presents a diagnostic approach to identifying UPIA in patients presenting with joint swelling, incorporating the dynamic nature of the condition with the potential to evolve over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".