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Record W1550567025 · doi:10.1155/2012/197319

Vascular Compromise and Hemodynamics in Pulmonary Arterial Hypertension: Model Predictions

2012· article· en· W1550567025 on OpenAlexaff
Zoheir Bshouty

Bibliographic record

VenueCanadian Respiratory Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineHemodynamicsCardiologyCompromiseInternal medicine

Abstract

fetched live from OpenAlex

Pulmonary circulation modelA computer model of the normal pulmonary circulation based on animal (dog) data obtained in the literature was previously developed by Bshouty and Younes (for details see references [S1, S2]).The model is described briefly and only details not included in the original model development will be included here.The original model is a multibranch model that bifurcates sequentially starting from the pulmonary artery (PA) up to eight (four generations) precapillary and capillary channels.The model was later modified to include five generations ending in sixteen precapillary and capillary channels (Figure S1).On the venous side, the vessels converge and reunite sequentially to end in the left atrium (LA).The resistance of a given vessel in the model is dependent on vessel dimensions (length and cross-sectional area).Changes in vessel dimensions occur in response to changes in lung volume and intravascular pressure (P v ).The original multibranch model was developed between 1987 and 1989, when microprocessors operated at 4.77 MHz.At the time, each extra-alveolar vessel was divided into twenty-five segments.This was done in order to shorten the time needed to go through the thousands of calculations and iterations needed to produce a stable solution (which at the time, took several minutes per one set of data).With the development of faster microprocessors, the number of segments of each extra-alveolar vessel was gradually increased initially to one hundred segments and is currently set at a thousand segments.Results obtained with twenty-five segments differed from results obtained with one hundred segments at the second decimal level and results obtained with a thousand segments differed from results obtained with one hundred segments mostly at the third decimal level and are therefore, not clinically significant.Similarly, results obtained with the four-generation model when compared with the five-generation model differed only at the second decimal level and are therefore, also not clinically significant.All simulations presented in this paper were generated with the five-generation model and 1000 segment vessels (See footnote).Cross-sectional area of each arterial (A a ) and venous (A v ) segment is calculated based on the transmural pressure (P tm ) across that segment, P tm being intravascular minus perivascular pressure (P x ).The characteristic behaviour of A a as a function of P tm was derived from data obtained by Smith and Mitzner (S3) and Maloney et al (S4) and described in detail by Bshouty and Younes (S1).It is expressed by the following linear relationship, SuppleMentary Material

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.264
Teacher spread0.224 · 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 designSimulation or modeling
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

Citations7
Published2012
Admission routes1
Has abstractyes

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