Treatment Algorithms for Systemic Sclerosis According to Experts
Bibliographic record
Abstract
OBJECTIVE: There is a lack of agreement regarding treatment for many aspects of systemic sclerosis (SSc). We undertook this study to generate SSc treatment algorithms endorsed by a high percentage of SSc experts. METHODS: Experts from the Scleroderma Clinical Trials Consortium and the Canadian Scleroderma Research group (n = 170) were asked whether they agreed with SSc algorithms from 2012. Two consensus rounds refined agreement; 62, 54, and 48 experts (36%, 32%, and 28%, respectively) completed the first, second, and third surveys, respectively. RESULTS: For treatment of scleroderma renal crisis, 81% of experts agreed (first-, second-, and third-line treatments were angiotensin-converting enzyme inhibitors, then adding calcium-channel blockers [CCBs], then adding angiotensin receptor blockers [ARBs], respectively). For pulmonary arterial hypertension (PAH), 81% of experts agreed (for mild PAH, treatments were phosphodiesterase 5 [PDE5] inhibitors, then endothelin receptor antagonists plus PDE5 inhibitors, then prostanoids, respectively; for severe PAH, prostanoids were first-line treatment). For mild Raynaud's phenomenon (RP), 79% of experts agreed (treatments were CCBs, then adding PDE5 inhibitors, then ARBs or switching to another CCB, respectively; after the third line of treatment, mild RP was deemed severe). For severe RP, the first- through fourth-line treatments were CCBs, then adding PDE5 inhibitors or prostanoids, then adding PDE5 inhibitors (if not added as second-line treatment) or prostanoids (if not added as second-line treatment), then switching to another CCB, respectively. For active treatment of digital ulcers, 66% of experts agreed (first- and second-line treatments were CCBs and PDE5 inhibitors, respectively). For interstitial lung disease, 69% of experts agreed (for induction therapy, treatments were mycophenolate mofetil [MMF], intravenous cyclophosphamide [IV CYC], and rituximab, respectively; for maintenance, first-line treatment was MMF). For skin involvement, 71% of experts agreed (for a modified Rodnan skin thickness score [MRSS] of 24, first- and second-line treatments were methotrexate [MTX] and MMF, respectively; for an MRSS of 32, first- through fourth-line treatments were MMF, MTX, IV CYC, and hematopoietic stem cell transplantation, respectively). For inflammatory arthritis, 79% of experts agreed (first- through fourth-line treatments were MTX, low-dose glucocorticoids, hydroxychloroquine, and rituximab or tocilizumab, respectively). Algorithms for cardiac and gastrointestinal involvement had ≥75% agreement. CONCLUSION: Total agreement for SSc algorithms was considerable. These algorithms may guide treatment.
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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 | 0.059 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".