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263.10: Exploring acceptability of lung uDCD among clinical and community stakeholders in New York City.

2024· article· en· W4402799415 on OpenAlexaboutno aff
Jingzhi Xu, Carolyn N. Sidoti, Mariam Girgis, Luis F. Angel, Stephanie H. Chang, Justin Chan, Brendan Parent, Macey L. Levan, Robert A. Montgomery, Stephen P. Wall

Bibliographic record

VenueTransplantation · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGerontology

Abstract

fetched live from OpenAlex

Introduction: The Toronto lung uncontrolled donation after circulatory death (uDCD) protocol used partial inflation with 20 cm H20 positive end-expiratory pressure and oxygen to preserve lungs noninvasively for up to 3 hours with 36% transplant yield after ex-vivo lung perfusion. The protocol required obtaining authorization for organ donation before lung preservation causing median 166 min delays in initiation. Transplant yield could markedly improve if authorized decision-makers within opt-in donation systems accept initiating noninvasive lung preservation without requiring prior permission. The study purpose was to vet this approach with clinical & community stakeholders in New York City (NYC).Methods: This qualitative focus group study occurred from July 2022 to March 2024 with community health workers and religious leaders served by and clinician working in emergency departments and ICUs of NYU Langone Brooklyn and Manhattan Hospitals. Participants were purposefully recruited to balance race/ethnicity, religious, and stakeholder groups. Moderators explained noninvasive lung preservation would begin without requiring prior permission regardless of organ donor registration status. For those without first person authorization, decision-makers could cease preservation or contemplate donor authorization. Participants discussed ethical permissibility of clinical procedures and authorization processes pertinent to stakeholders. Afterwards, participants completed demographic surveys with a 5-pt Likert question about support for the program. Digital audio recordings were transcribed verbatim and coded with NVivo (1.7.1). Iterative coding discerned themes, theoretical constructs, and summary narratives. Recruitment occurred until achieving thematic saturation. Results: We held 17 focus groups with 85 participants (3 to 8 per group). Of these, 84 (98.8%) supported the program. Among participants, 60% were female with nearly equal representation by race/ethnicity, religious, and stakeholder groups. Themes emerged representing clinical, ethical, legal, logistical, and public concerns. Clinical stakeholders voiced support for the program, but maintaining separation of clinical and preservation teams required personnel from other locations to initiate organ preservation expeditiously and have conversations with families. Clinical and community stakeholders mostly supported initiating preservation without prior permission, stating they “… will not be offended because it is saving somebody’s life.” Those initially voicing opposition supported the program after discussion. Most persons of faith were supportive even among those whose religions oppose brain death diagnosis. Participants voicing mistrust with organ donation believed the lung uDCD program was not offensive. Conclusions: Community and clinical stakeholders in NYC mostly voiced support for lung uDCD and initiating noninvasive lung preservation without requiring prior permission. National Institute of Health R61/R33 HL156890.

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)0.009
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.004
Scholarly communication0.0030.003
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.224
GPT teacher head0.385
Teacher spread0.160 · 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 designQualitative
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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