Phenotypes in pulmonary hypertension
Bibliographic record
Abstract
The clinical classification of pulmonary hypertension (PH) has guided diagnosis and treatment of patients with PH for several decades. Discoveries relating to underlying mechanisms, pathobiology and responses to treatments for PH have informed the evolution in this clinical classification to describe the heterogeneity in PH phenotypes. In more recent years, advances in imaging, computational science and multi-omic approaches have yielded new insights into potential phenotypes and sub-phenotypes within the existing clinical classification. Identification of novel phenotypes in pulmonary arterial hypertension (PAH) with unique molecular profiles, for example, could lead to new precision therapies. Recent phenotyping studies have also identified groups of patients with PAH that more closely resemble patients with left heart disease (group 2 PH) and lung disease (group 3 PH), which has important prognostic and therapeutic implications. Within group 2 and group 3 PH, novel phenotypes have emerged that reflect a persistent and severe pulmonary vasculopathy that is associated with worse prognosis but still distinct from PAH. In group 4 PH (chronic thromboembolic pulmonary disease) and sarcoidosis (group 5 PH), the current approach to patient phenotyping integrates clinical, haemodynamic and imaging characteristics to guide treatment but applications of multi-omic approaches to sub-phenotyping in these areas are sparse. The next iterations of the PH clinical classification are likely to reflect several emerging PH phenotypes and improve the next generation of prognostication tools and clinical trial design, and improve treatment selection in clinical practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".