Exercise-Induced Pulmonary Hypertension: How to Define, Diagnose, and Treat At-Risk Patients
Bibliographic record
Abstract
Introduction Pulmonary hypertension is a devastating disease with a rapid progression of symptoms leading to high patient mortality. It is characterized by high blood pressure in the pulmonary vasculature and poor pulmonary perfusion, resulting in patient fatigue, dyspnea, and syncope, especially upon physical exertion. A sub-clinical form of pulmonary hypertension also exists which is referred to as exercise induced pulmonary hypertension, where patients display normal resting hemodynamic properties but abnormal pulmonary responses to exercise. Discussion Recent evidence suggests early intervention and treatment of pulmonary hypertension can improve patient outcomes. However, there is a lack of clinical evidence supporting effective treatments for exercise induced pulmonary hypertension (EIPH), arguably the earliest stage of pulmonary hypertension. This is due in part to the removal of EIPH from official guidelines such as the European Respiratory Society in 2008. EIPH was removed from clinical guidelines due to a lack of consensus on the definition and standardized testing procedures for diagnosing EIPH. Emerging evidence suggests that exercise testing following a standardized protocol of stress echocardiography or right heart catheterization of patients may allow for the classification of EIPH as a mean pulmonary artery pressure/cardiac output slope > 3 mmHg/L/min, and/or mean pulmonary artery pressure > 30 mmHg with a pulmonary vascular resistance > 3 Wood Units. Conclusion Providing evidence for a consensus definition of EIPH, along with a validated, standardized testing procedure, will hopefully foster the progression of research on EIPH and further the development of treatments and improve patient outcomes for people with pulmonary hypertension.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".