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Record W4415217717 · doi:10.1016/j.jhlto.2025.100409

Pretransplant screening for infections in lung and heart transplant candidates

2025· review· en· W4415217717 on OpenAlexaff
Felix Riunga, Carlos Cervera, Dima Kabbani

Bibliographic record

VenueJHLT Open · 2025
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLungLung transplantationDiseaseSolid organInfectious disease (medical specialty)Heart-Lung TransplantationTransplantationOrgan transplantation

Abstract

fetched live from OpenAlex

Solid organ transplantation (SOT) is the definitive treatment for end-stage organ disease but also introduces a heightened risk of infection in the post-transplant period. In heart and lung transplantation, unique factors predisposing to infection include exposure of the lungs to the external environment, alteration of airway anatomy, pretransplant infection and colonization with difficult-to-treat organisms, and pretransplant presence of infected devices. This is in addition to risks common to all SOT leading, including immunosuppression. The aim of pretransplant infectious disease screening in SOT recipients is to prospectively identify infectious risks through careful history taking, examination, and laboratory testing, as well as mitigating these risks through vaccination, treatment, prophylaxis, and counseling on safe living. In this article, we review infectious risks in heart and lung transplant candidates and discuss recommendations for screening before transplantation.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.085
GPT teacher head0.456
Teacher spread0.371 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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