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Record W4386574382 · doi:10.1016/j.chpulm.2023.100020

Automated CT-Based Quantification of Pulmonary Veins Shows Greater Central Venous Dilation in Group 2 Pulmonary Hypertension Compared With Group 1 Pulmonary Arterial Hypertension and Control Subjects

2023· article· en· W4386574382 on OpenAlexaboutno aff
A. Synn, Eileen M. Harder, Pietro Nardelli, James C. Ross, Bradley A. Maron, Jane A. Leopold, Aaron B. Waxman, Raúl San Jośe Estépar, George R. Washko, Farbod N. Rahaghi

Bibliographic record

VenueCHEST Pulmonary · 2023
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsnot available
FundersAstellas PharmaBoston Biomedical Innovation CenterNational Heart, Lung, and Blood InstituteAbbott VascularActelion PharmaceuticalsRegeneron Pharmaceuticals
KeywordsPulmonary hypertensionMedicineDilation (metric space)CardiologyInternal medicine

Abstract

fetched live from OpenAlex

Pulmonary hypertension (PH) is a heterogeneous disease that includes pulmonary arterial hypertension (PAH; World Symposium PH group 1); however, PH can also result from left-sided heart disease (pulmonary venous hypertension [PVH]; World Symposium PH group 2).1 Although both groups demonstrate hemodynamic abnormalities on invasive right heart catheterization (RHC), the management approach, particularly pulmonary vasodilator therapy, differs considerably for patients with PVH compared with PAH.1 Given that PVH is the most prevalent form of PH and is increasing in incidence,2 noninvasive methods of differentiating PVH from PAH potentially may improve screening strategies for this large and growing population.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.034
GPT teacher head0.254
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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