Development of a self-administered early inflammatory arthritis detection tool
Bibliographic record
Abstract
BACKGROUND: Barriers to care limit the potential benefits of pharmacological intervention for inflammatory arthritis. A self-administered questionnaire for early inflammatory arthritis (EIA) detection may complement contemporary triage interventions to further reduce delays to rheumatologic care. The objective of this study was to develop a self-administered EIA detection tool for implementation in pre-primary care settings. METHODS: A core set of dimensions and constructs for EIA detection were systematically derived from the literature and augmented by investigative team arbitration. Identified constructs were formulated into lay language questions suitable for self-administration. A three-round Delphi consensus panel of EIA experts and stakeholders evaluated the relevance of each question to EIA detection and suggested additional items. Questions accepted by less than 70% of respondents in rounds one or two were eliminated. In round three, questions accepted by at least 80% of the panel were selected for the tool. RESULTS: Of 584 citations identified, data were extracted from 47 eligible articles. Upon arbitration of the literature synthesis, 30 constructs encompassing 13 dimensions were formulated into lay language questions and posed to the Delphi panel. A total of 181 EIA experts and stakeholders participated on the Delphi panel: round one, 60; round two, 59; and, round three, 169; 48 participated in all three rounds. The panel evaluated the 30 questions derived from the literature synthesis, suggested five additional items, and eliminated a total of 24. The eleven-question instrument developed captured dimensions of articular pain, swelling, and stiffness, distribution of joint involvement, function, and diagnostic and family history. CONCLUSIONS: An eleven-question, EIA detection tool suitable for self-administration was developed to screen subjects with six to 52 weeks of musculoskeletal complaints. Psychometric and performance property testing of the tool is ongoing.
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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 | 0.066 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".