Bibliographic record
Abstract
I n the past decade, there have been remarkable advances in the medical management of patients with idiopathic pulmonary arterial hypertension (IPAH).These therapies have improved the quality of life, saved many lives, and, in some cases, obviated the need for lung transplantation.However, the reality is that the majority of patients with IPAH, even with access to the best medical therapy, continue to progress and will require lifesaving lung transplantation. Article see p 2503Lung transplantation for IPAH has traditionally been viewed as a higher up-front surgical-risk lung transplant to perform, but with excellent long-term survival and quality of life.The concern in the current management strategy for patients with IPAH is that, by virtue of the seriousness of the condition, they tend to have the highest mortality on the lung transplant waitlist, and, then, with the practice of listing them after they begin to fail first-, second-, and sometimes third-line medical therapies, they are in even poorer medical condition and, in general, are deteriorating very rapidly.With a limited supply of donor organs, onc can see that this might exacerbate the problem of waitlist mortality in this particular patient population that cannot afford to wait.One can also appreciate then, that when the United Network for Organ Sharing Lung Allocation Score (LAS) was initially introduced, this caused some concern regarding the potential for placing IPAH patients on the list at a disadvantage.The LAS score was designed with the intent to optimize the use of the scarce resource, donor lungs, by the allocation of lungs to patients who have the best potential for a good outcome after transplantation, balancing mortality on the waitlist with survival probability after transplant.To optimally treat patients with IPAH, the treating physicians in the IPAH and lung transplant community need to (1) optimize medical care, (2) optimize timing of listing for transplant, and (3) optimize management on the lung transplant waitlist to maximize the likelihood of successful lifesaving lung transplantation in the context of available donor organs and current listing guidelines and practices.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.022 | 0.019 |
| 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".