Can machine learning assist in systemic sclerosis diagnosis and management? A scoping review
Bibliographic record
Abstract
This scoping review aims to summarize the existing literature on how machine learning can be used to impact systemic sclerosis diagnosis, management, and treatment. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines, Embase, Web of Science, Medline (PubMed), IEEE Xplore, and ACM Digital Library were searched from inception to 3 March 2024, for primary literature reporting on machine learning models in any capacity regarding scleroderma. Following robust triaging, 11 retrospective studies were included in this scoping review. Three studies focused on the diagnosis of scleroderma to influence preferred management and nine studies on treatment and predicting treatment response to scleroderma. Nine studies used supervision in their machine learning model training; two used supervised and unsupervised training and one used solely unsupervised training. A total of 817 patients were included in the data sets. Seven of the included articles used patients from the United States, one from Belgium, two from Japan, and two from China. Although currently limited to retrospective studies, the results indicate that machine learning modeling may have a role in early diagnosis, management, therapeutic decision-making, and in the development of future therapies for systemic sclerosis. Prospective studies examining the use of machine learning in clinical practice are recommended to confirm the utility of machine learning in patients with systemic sclerosis.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".