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Record W2131280062 · doi:10.1164/rccm.201401-0168cp

Ventilator-induced Lung Injury. Similarity and Differences between Children and Adults

2014· article· en· W2131280062 on OpenAlexafffund
Martin C. J. Kneyber, Haibo Zhang, Arthur S. Slutsky

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineSimilarity (geometry)LungIntensive careIntensive care medicineEmergency medicineInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

It is well established that mechanical ventilation can injure the lung, producing an entity known as ventilator-induced lung injury (VILI). There are various forms of VILI, including volutrauma (i.e., injury caused by overdistending the lung), atelectrauma (injury due to repeated opening/closing of lung units), and biotrauma (release of mediators that can induce lung injury or aggravate pre-existing injury, potentially leading to multiple organ failure). Experimental data in the pediatric context are in accord with the importance of VILI, and appear to show age-related susceptibility to VILI, although a conclusive link between use of large Vts and mortality has not been demonstrated in this population. The relevance of VILI in the pediatric intensive care unit population is thus unclear. Given the physiological and biological differences in the respiratory systems of infants, children, and adults, it is difficult to directly extrapolate clinical practice from adults to children. This Critical Care Perspective analyzes the relevance of VILI to the pediatric population, and addresses why pediatric patients might be less susceptible than adults to VILI.

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)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.308
Teacher spread0.292 · 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

Citations156
Published2014
Admission routes2
Has abstractyes

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