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Record W3197630020 · doi:10.1097/txd.0000000000001209

Lung Transplantation in Systemic Sclerosis: a Practice Survey of United States Lung Transplant Centers

2021· article· en· W3197630020 on OpenAlexaff
Sameep Sehgal, Kelly M. Pennington, Huaqing Zhao, Cassie C. Kennedy

Bibliographic record

VenueTransplantation Direct · 2021
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineGastroparesisLungLung transplantationInternal medicineTransplantationDiseaseBody mass indexIntensive care medicineSurgeryStomach

Abstract

fetched live from OpenAlex

Lung transplantation in patients with systemic sclerosis (SSc) can be complicated by extrapulmonary manifestations of the disease, leading to concerns regarding posttransplant complications and outcomes. METHODS: We conducted a web-based survey of adult lung transplant programs in the United States regarding their practices in patients with SSc. RESULTS: Sixty percent (37/62) of the eligible centers responded to the survey, majority of the respondents were medical directors (81%). Most centers would consider transplanting patients with mild or moderate esophageal disease (92% or 75%, respectively) or gastroparesis (59%). A minority would consider patients with severe esophageal dysmotility (37%), digital ulcers (21%), or low body mass index (19%). Most centers conducted extensive pretransplant gastrointestinal evaluation and use a conservative feeding approach with prolonged nothing by mouth (83%) and postpyloric feeding (89%). Antireflux surgery is commonly considered (40%) with partial fundoplication being the procedure of choice (67%). Most respondents expected similar outcomes of acute or chronic rejection (81% and 51%, respectively), respiratory infections (76%), and 1-year survival (70%). CONCLUSIONS: Most US lung transplant centers do not universally exclude SSc from lung transplant listing, but most support extensive pretransplant gastrointestinal testing and a conservative approach to feeding in the early posttransplant period.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.041
GPT teacher head0.328
Teacher spread0.287 · 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.

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

Citations7
Published2021
Admission routes1
Has abstractyes

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