Classification criteria for systemic sclerosis subsets.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the measurement properties of criteria for systemic sclerosis (SSc) subsets for classification of patients in SSc trials, and to determine if any one criteria set confers measurement advantage over others. METHODS: A systematic review of articles describing classification criteria for SSc subsets was performed. Evidence supporting the sensibility (statement of purpose for which the criteria will be used, population, setting, face and content validity, and feasibility), validity, and reliability of the criteria was evaluated. RESULTS: Fourteen sets of criteria for SSc subsets were identified. There is variability in the intended purpose and setting for which criteria sets are to be applied. Although face validity improves with the addition of less commonly encountered subsets or disease manifestations as criteria, the feasibility of implementing such criteria is conversely limited. Content validity for most criteria sets has not been evaluated due to lack of an explicitly stated conceptual framework for SSc. The criteria with 3 or more subsets do not provide incremental predictive validity over the 2-subset criteria. Our ability to compare subset criteria on divergent validity and reliability is limited by a lack of data. CONCLUSION: The 2-subset criteria of LeRoy, et al have good feasibility, acceptable face validity, and good predictive validity. Further research is needed to compare the content validity, divergent validity, and reliability of these with other subset criteria for use in SSc trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".