Approaches for <i>Mycobacterium tuberculosis</i> Transmission Inference Based on Genomic Data
Bibliographic record
Abstract
Those with higher PAWP generally had similar responses (which approximated the minimal clinically important difference) across mPAP levels (14).These findings are important, as an attenuated effect of PAH therapies was shown in the presence of systemic hypertension, diabetes, or coronary artery disease (15).In addition, in clinical practice, patients with elevated left-sided filling pressures can worsen with PAH therapies.Our findings suggest that patients with PAH with PAWP toward the upper end of the range currently used to support a diagnosis of PAH are not predisposed to adverse effects and respond similarly to the therapies studied when considering traditional RCT endpoints.Our analysis included only patients with PAH who were enrolled in RCTs, which exclude other chronic heart and/or lung diseases.As a consequence, our results may not be generalizable to an unselected cohort of "real-life" patients with PAH, in whom the average age and the burden of comorbidities may be greater.Importantly, all patients need thorough clinical evaluation before the initiation of PAH treatment and close observation during follow-up.Clinical decisions should not be made based on a single hemodynamic parameter such as PAWP but rather with consideration of the entire presentation of the patient (16).In conclusion, the analysis of the individual data of more than 4,000 patients from 14 prospective, placebo-controlled randomized PAH trials revealed no significant interaction between baseline PAWP and treatment effects, supporting the current PAWP threshold for the hemodynamic definition of precapillary pulmonary hypertension.
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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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".