A comparison between general rheumatologists and scleroderma experts with respect to following systemic sclerosis guidelines.
Bibliographic record
Abstract
OBJECTIVES: To determine if there are differences between expert and non-expert rheumatologists in systemic sclerosis (SSc) management. METHODS: Information relating to demographics, complications, investigations, and treatment of SSc patients was obtained from an online survey to members of the Canadian Rheumatology Association (CRA), and selected chart audits. Results were compared to data from a SSc database ('experts', Canadian Scleroderma Research Group--CSRG). RESULTS: The online survey (61/300 respondents; 20% response rate) found that most agreed with the EULAR SSc guidelines. Some exceptions were only 47% said they ordered annual echocardiograms and 45% pulmonary function tests. Chart audits of 70 SSc patients from 7 community rheumatology practices revealed no significant differences in their treatment from SSc guidelines, but some investigations differed compared to the CSRG. There was site variability among community practices relating to investigations, and treatment. Patients receiving an echocardiogram within the previous year varied from 10-90%, and PA pressure was reported in 30-100% of SSc patients among sites. Overall, 91% of SSc patients on chart audit had ever received an echocardiogram, but in 30% of cases there was no PA pressure recorded vs. only 19% in CSRG (p=0.001). CONCLUSIONS: Compared to SSc experts, general rheumatologists did not differ in their practices for many SSc guidelines despite the fact that they do not see many SSc patients when compared to SSc experts, but there was site variability. An apparent difference is that although echocardiograms are being ordered, PA pressures are missing which could lead to late detection of PAH.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".