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Record W2129967018 · doi:10.1186/s13054-014-0612-z

Advancing the science of organ donor management

2014· letter· en· W2129967018 on OpenAlexaff
Sonny Dhanani, Sam D. Shemie

Bibliographic record

VenueCritical Care · 2014
Typeletter
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsMontreal Children's HospitalMcGill UniversityChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineOrgan donationRigourObservational studyIntensive care medicineDeclarationDonationTransplantationSurgeryPathology

Abstract

fetched live from OpenAlex

There is an increasing burden of responsibility for intensivists to optimize donation potential after the declaration of brain death in patients with catastrophic brain injury. Best practice for donor management, if present, has been formed on low quality and mainly observational studies or consensus. In particular, research into the use of corticosteroids has shown varied benefit. The specific and limited results of the CORTICOME study are less important than the systematic methodology and the development of rigour in the study of deceased organ donation. Donor management would benefit from continued systematic analysis of current literature, understanding of the physiologic basis for therapy, and further prospective controlled trials. Worldwide collaboration partnerships and funding are needed to optimize the management of deceased organ donation.

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)0.048
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: Editorial · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.048
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.008
Scholarly communication0.0050.009
Open science0.0020.004
Research integrity0.0260.046
Insufficient payload (model declined to judge)0.0070.005

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 head0.015
GPT teacher head0.306
Teacher spread0.292 · 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
GenreEditorial

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

Citations16
Published2014
Admission routes1
Has abstractyes

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