Pulmonary capillary recruitment in exercise and pulmonary hypertension
Bibliographic record
Abstract
We read with interest the excellent European Respiratory Society statement on exercise pulmonary hypertension (PH) by Kovacs et al. [1] in the European Respiratory Journal . Fundamentally, exercise PH, especially with precapillary causes such as pulmonary arterial hypertension (PAH), is an inability of the lung circulation to accommodate increased blood flow during exercise. Although the authors mention “distention” of the vasculature, implying stretching of already perfused vessels, the increased pulmonary blood flow is primarily accommodated in the normal lung by recruitment of unused capillaries, allowing the pulmonary artery pressure to change minimally during exercise [2, 3]. Further evidence of this recruitment is found in true vasoresponders during an acute vasodilator challenge for evaluation of idiopathic PAH [4]. By contrast, because it is caused by precapillary vascular obstruction and not vasoconstriction, vasodilator-nonresponsive PAH accommodates any increased cardiac output via distention and not via recruitment [5]. We have also observed pulmonary capillary recruitment in normal humans during exercise (unpublished data). Recruitment is a normal physiological process, and it is impaired in many types of PH. As we move forward in our understanding of exercise PH and its physiology, precise definitions and semantics will be critical. Pulmonary capillary recruitment is important in the pulmonary haemodynamic response to exercise
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".