Patient Perspectives and Clinical Insights Into the Diagnostic Journey From Connective Tissue Disease to Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Plain Language SummaryPeople with connective tissue disorders (CTDs), like systemic lupus erythematosus (SLE), mixed CTD, and systemic sclerosis (SSc), are at risk for a lung condition called pulmonary arterial hypertension (PAH). Since PAH gets worse over time if it is not treated, it is important for anyone with PAH to be diagnosed and treated with medication as early as possible. This article describes the experiences of 4 adults who have CTD and PAH, including their journey of being diagnosed with CTD and then PAH, which healthcare providers they saw, and the types of information they were given to learn about their conditions. All 4 people described having a rash as their first symptom of CTD, and said their symptoms got worse over time, which made them see their primary care provider. Two people felt their doctor dismissed their symptoms. Everyone was eventually referred to a rheumatologist or dermatologist and diagnosed with a type of CTD: 1 had SLE, 2 had mixed CTD, and 1 had SSc. Only 1 person was told that having a CTD meant they had a higher risk of getting PAH. It took between 2 and 11 years after their CTD diagnosis to be diagnosed with PAH, all while they were at risk for more intense PAH symptoms. The group recommended better education for doctors and support staff about CTD, so that they are able to screen their patients for PAH, recognize the disease, and quickly refer patients to PAH specialists at accredited pulmonary hypertension centers for further testing.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".