Generation of a Core Set of Items to Develop Classification Criteria for Scleroderma Renal Crisis Using Consensus Methodology
Bibliographic record
Abstract
OBJECTIVE: To generate a core set of items to develop classification criteria for scleroderma renal crisis (SRC) using consensus methodology. METHODS: An international, multidisciplinary panel of experts was invited to participate in a 3-round Delphi exercise developed using a survey based on items identified by a scoping review. In round 1, participants were asked to identify omissions and clarify ambiguities regarding the items in the survey. In round 2, participants were asked to rate the validity and feasibility of the items using Likert-type scales ranging from 1 to 9 (where 1 = very invalid/unfeasible, 5 = uncertain, and 9 = very valid/feasible). In round 3, participants reviewed the results and comments from round 2 and were asked to provide final ratings. Items rated as highly valid and feasible (median scores ≥7 for each) in round 3 were selected as the provisional core set of items. A consensus meeting using a nominal group technique was conducted to further reduce the core set of items. RESULTS: Ninety-nine experts from 16 countries participated in the Delphi exercise. Of the 31 items in the survey, consensus was achieved on 13, in the categories hypertension, renal insufficiency, proteinuria, and hemolysis. Eleven experts took part in the nominal group technique discussion, where consensus was achieved in 5 domains: blood pressure, acute kidney injury, microangiopathic hemolytic anemia, target organ dysfunction, and renal histopathology. CONCLUSION: A core set of items that characterize SRC was identified using consensus methodology. This core set will be used in future data-driven phases of this project to develop classification criteria for SRC.
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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 | 0.179 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.021 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".