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DONATE

2017· article· en· W2751538311 on OpenAlexaffabout
Frédérick D’Aragon, Anne Julie Frenette, François Lamontagne, Ian Ball, Aemal Akhtar, Erika Arseneau, Lori Hand, Marie-Hélène Masse, Bram Rochwerg, David Williamson, Sonny Dhanani, Maureen O. Meade

Bibliographic record

VenueTransplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of OttawaWestern UniversityUniversité de SherbrookeMcMaster UniversityImpactUniversité de Montréal
Fundersnot available
KeywordsMedicineOrgan donationObservational studyDonationInformed consentPsychological interventionTransplantationIntensive care medicineIntensive careClinical trialFamily medicineEmergency medicineMedical emergencySurgeryInternal medicineNursingAlternative medicine

Abstract

fetched live from OpenAlex

Introduction Transplantation is a definitive treatment option for many people suffering from end-stage organ failure. Organ supply, however, far exceeds the supply. While recent studies suggest that improved management of deceased organ donors may increase transplant rates and allograft function, research to support best practices in donor management is sparse. The DONATE Pilot Observational Study aims to identify and to address logistical and regulatory complexities prior to embarking upon a national observational study that will improve the care of organ donors and lay the foundation for future clinical trials. Methods This 12 month prospective cohort study was conducted between September 2015 - April 2017 in the intensive care units (ICUs) of 8 Ontario and Quebec hospitals enrolled consecutively consented adult deceased donors [donation after neurological determination of death (DND) and donation after circulatory determination of death (DCD). The primary outcomes of this pilot study included the successful application of a waived research consent model, the time to complete the case report forms (CRF) and the completeness of transplant recipient data. Detailed data from various hospital sources and provincial organ donation organizations included death determination procedures, cardiopulmonary monitoring technologies, basic and advanced life sustaining therapy, general prophylactic interventions, organ donation specific therapies and clinical complications, defined a priori, from one day prior to donation consent up to the time of organ recovery. We also recorded specific limited data about the transplant recipients. Results In collaboration with ethicists, lawyers and privacy officers, all hospitals obtained authorization to implement this study with a waiver of research consent. The study, includes 180 consecutive deceased donors (58% DND; 42% DCD). The most common critical illness leading to organ donation was global anoxic brain injury (n = 37, 20.,6%). The mean time from donation consent to organ recovery was 2.39 days (SD = 0.72). Research coordinators estimated that prospective data completion required 5-7 hours per participant, but retrospective approaches required more time. With formal data sharing agreements, we obtained recipient data. Conclusion This pilot study identified and addressed some unique logistical and regulatory organ donation research challenges. The process provides a framework for addressing the same issues, and refining data collection, in the context of an upcoming national prospective cohort study. Clinical findings from the national cohort will inform the design of future trials in organ donor management. Canadian Critical Care Trials Group. Canadian National Transplant Research Program.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.523
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.5230.252

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 head0.024
GPT teacher head0.317
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes2
Has abstractyes

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