Pulmonary arterial hypertension screening of systemic sclerosis patients in clinical practice: an independent chart review
Bibliographic record
Abstract
Patients with systemic sclerosis (SSc) are at increased risk of pulmonary arterial hypertension (PAH). Guidelines recommend annual screening with pulmonary function testing (PFT) and transthoracic echocardiogram (TTE). Through auditing the charts of 11 rheumatologists associated with McMaster University, we evaluated the proportion of SSc patients without PAH or pulmonary fibrosis who receive annual TTE, PFT, and dyspnea screening. Screening rates between self-identified SSc experts and non-experts were compared. In cases where screening tests were abnormal, charts were reviewed for evidence of cardiologist or respirologist referral. In total, 136 patients’ charts were included. Annual screening for dyspnea was very common (88% of patients, 119/134). Annual PAH screening via TTE (74%, 100/135) and PFT (79%, 107/136) was less common. Annual dyspnea screening, TTE, and PFT were more commonly performed by SSc experts than by non-experts (94% vs. 83%, p = 0.03; 85% vs. 61%, p = 0.002; 93% vs. 62%, p<0.001, respectively). Nearly all patients with an abnormal TTE (10/11, 91%) and PFT (12/14, 86%) received appropriate referrals. Future research should explore reasons for differences in screening rates between SSc experts and non-experts. Given that rheumatologists screen for dyspnea more often than they order PFT and TTE, there may be barriers to ordering these tests that warrant further investigation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".