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Record W2572825480 · doi:10.9778/cmajo.20160093

Time trends in organ donation after neurologic determination of death: a cohort study

2017· article· en· W2572825480 on OpenAlexafffundvenueabout
Andreas H. Kramer, Ryan Baht, Christopher J. Doig

Bibliographic record

VenueCMAJ Open · 2017
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of Calgary
FundersCanadian Blood Services
KeywordsOrgan donationTraumatic brain injuryMedicineBrain deadCohortPopulationStroke (engine)TransplantationCause of deathInternal medicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The cause of brain injury may influence the number of organs that can be procured and transplanted with donation following neurologic determination of death. We investigated whether the distribution of causes responsible for neurologic death has changed over time and, if so, whether this has had an impact on organ quality, transplantation rates and recipient outcomes. METHODS: We performed a cohort study involving consecutive brain-dead organ donors in southern Alberta between 2003 and 2014. For each donor, we determined last available measures of organ injury and number of organs transplanted, and compared these variables for various causes of neurologic death. We compared trends to national Canadian data for 2000-2013 (2000-2011 for Quebec). RESULTS: = 0.002). INTERPRETATION: Anoxic brain injury has become a leading cause of organ donation after neurologic determination of death in Canada. Organs from donors with anoxic brain injury have a greater degree of injury, and fewer are transplanted. These findings have implications for availability of organs for transplantation in patients with end-stage organ failure.

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)0.000
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 score0.014
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.031
GPT teacher head0.336
Teacher spread0.305 · 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

Citations22
Published2017
Admission routes4
Has abstractyes

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