Bibliographic record
Abstract
The search for effective treatments for pulmonary hypertension (PH) has been frustrating. The very first drug for idiopathic pulmonary arterial hypertension (PAH), epoprostenol, was approved in 1995.[1] And while progress has been made, a look at where we were, where we are, and where we need to be suggests we are far from accomplishing our goals.[2] We have identified three classes of drugs that improve symptoms, but we still do not have drugs that modify the disease process or protect patients from developing progressive pulmonary vascular disease.[3] Perhaps this is because the developed drugs were studied due to their vasodilator properties, while scientific research has now demonstrated that cellular proliferation, inflammation, and thrombosis are the dominant underlying pathobiologic processes, with chronic pulmonary vasoconstriction playing a relatively minor role.[4] In a recent editorial, Dr. Joseph Loscalzo pointed to the continuing low level of drugs approved by the FDA over the past 11 years in the face of an increasing demand for personalized cardiovascular drug development.[5] His call for a new paradigm has been echoed by academics, regulatory agencies, and the pharmaceutical industry. However, with limited patients and the high cost of drug development, it has become obvious that we have to find ways to identify promising drugs more quickly, with trials that require smaller numbers of patients, and shortened times to approval if we expect the pharmaceutical industry to continue to invest in treatments for this disease.[6] In return, our industry partners must be willing to embrace a paradigm that requires data sharing and collaboration.[7]To address this challenge, the Pulmonary Hypertension Academic Research Consortium (PHARC) was created as a forum to openly discuss strategies for clinical trials in PH that would benefit all of the stakeholders. The consortium initially included academics with an interest in pulmonary vascular disease, regulatory authorities from the United States, Canada, and Mexico, members of pharmaceutical companies with approved or developing drugs for PH, and observers from medical societies from
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.261 | 0.165 |
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".