MétaCan
Menu
Back to cohort
Record W3012045454 · doi:10.21037/atm.2020.03.102

Infection prophylaxis and management of fungal infections in lung transplant

2020· review· en· W3012045454 on OpenAlex
Armelle Pérez-Cortés Villalobos, Shahid Husain

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Translational Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineLung transplantationLungIncidence (geometry)Intensive care medicineAspergillusAspergillosisCandida infectionsTransplantationAntifungalImmunologyInternal medicineBiologyDermatologyMicrobiology

Abstract

fetched live from OpenAlex

Lung transplantation has emerged as a lifesaving treatment for a wide range of advanced lung diseases. While the survival of lung transplant recipients continues to improve, infectious complications contribute substantially to morbidity and mortality following lung transplantation. The incidence of invasive fungal infections is variable, with a mean occurrence of 8.6%. The majority of fungal infections in lung transplant recipients are caused Aspergillus and Candida species. This review provides an update in the current approaches for the diagnosis, management and prevention of fungal infections and the late complications that are associated.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.836
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.148
GPT teacher head0.446
Teacher spread0.298 · 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