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Record W4396806196 · doi:10.1016/j.jhlto.2024.100107

Body mass index and mortality following primary graft dysfunction: A Lung Transplant Outcomes Group study

2024· article· en· W4396806196 on OpenAlexfundno aff
Rachel M Bennett, John P. Reilly, Joshua M. Diamond, Edward Cantu, M.G.S. Shashaty, Luke Benvenuto, Jonathan P. Singer, Scott M. Palmer, Jason D. Christie, Michaela R. Anderson

Bibliographic record

VenueJHLT Open · 2024
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsnot available
FundersNational Institutes of HealthCareDxUnited Therapeutics CorporationBristol-Myers SquibbCSL BehringInternational Society for Heart and Lung TransplantationBoomer Esiason FoundationNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteMallinckrodt PharmaceuticalsU.S. Department of Defense
KeywordsBody mass indexMedicineLungIndex (typography)Internal medicineLung transplantationPrimary (astronomy)Cardiology

Abstract

fetched live from OpenAlex

Higher body mass index (BMI) increases the risk of developing primary graft dysfunction (PGD) after lung transplantation; whether BMI is associated with decreased survival after PGD is unknown. We utilized the Lung Transplant Outcomes Group cohort of 1,538 subjects from 2011-2018. We evaluated the association between preoperative BMI and graft survival among subjects with severe PGD using Cox proportional hazards models with linear splines. Models were stratified by center and adjusted for sex, age, Lung Allocation Score, and diagnosis. PGD developed in 383 subjects. Among subjects with PGD, low BMI was associated with increased mortality while high BMI was not associated with differential mortality, compared to normal BMI. Results were similar for 90-day and 1-year survival. While high BMI increases the risk of developing PGD, it does not appear to be associated with survival after PGD. Future work should focus on PGD prevention rather than PGD management in patients with obesity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.035
GPT teacher head0.375
Teacher spread0.340 · 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 teacher head, 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

Citations4
Published2024
Admission routes1
Has abstractyes

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