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Record W2017982207 · doi:10.1007/s12576-013-0298-z

Changes in systemic and pulmonary blood flow distribution in normal adult volunteers in response to posture and exercise: a phase contrast magnetic resonance imaging study

2014· article· en· W2017982207 on OpenAlexaff
Derek T. H. Wong, Kyong-Jin Lee, Shi‐Joon Yoo, George Tomlinson, Lars Grosse‐Wortmann

Bibliographic record

VenueThe Journal of Physiological Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsToronto General HospitalSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMagnetic resonance imagingContrast (vision)MedicineBlood flowPhase contrast microscopyDistribution (mathematics)CardiologyNuclear magnetic resonanceInternal medicineRadiologyPhysicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Hemodynamics are usually evaluated in the supine position at rest. This is only a snapshot of an individual's daily activities. This study describes circulatory adaptation, as assessed by magnetic resonance imaging, to changes in position and exercise. Phase contrast magnetic resonance imaging of blood flow within systemic and pulmonary arteries and veins was performed in 24 healthy volunteers at rest in the prone and supine position and with bicycle exercise in the supine position. No change was seen in systemic blood flow when moving from prone to supine. Exercise resulted in an increased percentage of cardiac output towards the lower body. Changes in position resulted in a redistribution of blood flow within the left lung--supine positioning resulted in decreased blood flow to the left lower pulmonary vein. With exercise, both the right and left lower lobes received increased blood flow, while the upper lobes received less.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.285
Teacher spread0.272 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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