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Record W4415595812 · doi:10.7764/tesisuc/med/104540

Metabolic effects on lung parenchyma of chronic hypoxia secondary to its extrinsic compression in an animal model of congenital diaphragmatic hernia

2024· dissertation· W4415595812 on OpenAlexaboutno aff
Francisco Márquez

Bibliographic record

Venuenot available
Typedissertation
Language
FieldMedicine
TopicCongenital Diaphragmatic Hernia Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCongenital diaphragmatic herniaExtracorporeal membrane oxygenationMortality rateFetusDiaphragmatic breathingHypoxia (environmental)Diaphragmatic herniaHerniaNeonatal mortalitySurvival rate

Abstract

fetched live from OpenAlex

Congenital diaphragmatic hernia (CDH) occurs as a consequence of abnormal development of the transverse septum and incomplete closure of the pleuroperitoneal canals that occurs between the 6th and 10th week of gestation, resulting in herniation of the abdominal viscera through the diaphragmatic defect. The incidence of CDH is 1.93/10.000 births in North America, with an overall 45.89% mortality in the first year of life. In other latitudes of the world, it is similar, with a prevalence, for example, in Canada of 3.38 per 10,000 and in Chile of 2.1 per 10,000 live births, with a mortality rate in the first year of life between 45 and 65%. Despite all the advances of the last 20 years in maternal-fetal medicine, neonatology, and pediatric surgery, mortality remains high at 45-65% in CDH patients. In the prenatal period, timely diagnosis and the attempt to classify fetuses with CDH according to their risk of death and prognosis have been a constant challenge in maternalfetal medicine (MFM). Even advanced ventilatory and circulatory support techniques, such as extracorporeal membrane oxygenation (ECMO), used in the neonatal period have not significantly influenced the mortality rate of CDH.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.305
Teacher spread0.285 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

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