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Record W2259605271 · doi:10.1152/jappl.2000.88.2.705

Ventilation heterogeneity does not change following pulmonary microembolism

2000· article· en· W2259605271 on OpenAlexaff
John Y. C. Tsang, David M. Frazer, Michael P. Hlastala

Bibliographic record

VenueJournal of Applied Physiology · 2000
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersNational Heart, Lung, and Blood Institute
KeywordsVentilation (architecture)SalineLungAnesthesiaWashoutMedicineChemistryInternal medicine

Abstract

fetched live from OpenAlex

By using the multiple-breath helium washout technique, ventilation heterogeneity (VH) after embolic injury in the lung can be quantitatively partitioned into the conductive and acinar components. Total VH, represented by the normalized slope of the phase III alveolar plateau, Sn(III (total)), was studied for 120 min in three groups of anesthetized and paralyzed mongrel dogs. Group 1 (n = 3) received only normal saline and served as controls. Group 2 (n = 4) received repeated infusions of polystyrene beads (250 microm) into the right atrium at 10, 40, 80, and 120 min. Group 3 (n = 3) was similarly treated, except that the embolic beads used were 1,000 microm in diameter. The data show that, despite repeated embolic injury by polystyrene beads of different diameters, there was no significant increase in total VH. The acinar component of Sn(III), which represents VH in the distal airways, accounts for over 90% of the total VH. The conductive component of Sn(III), which represents VH between larger conductive airways, remains relatively constant and a minor component. We conclude that pulmonary microembolism does not result in significant redistribution of ventilation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 head0.025
GPT teacher head0.278
Teacher spread0.252 · 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

Citations13
Published2000
Admission routes1
Has abstractyes

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