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Record W4413115797 · doi:10.1016/j.jhlto.2025.100349

Priorities for lung transplantation research: a James Lind Alliance priority-setting partnership between patients, caregivers, and clinicians in Canada

2025· article· en· W4413115797 on OpenAlexaffabout
K. Halloran, Lea Harper, Nikki J. Marks, Céline Bergeron, Basil Nasir, Dima Kabbani, Laura van den Bosch, A. Hirji, Rhea Varughese, Jason Weatherald

Bibliographic record

VenueJHLT Open · 2025
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsVancouver General HospitalUniversity Health NetworkToronto General HospitalCentre Hospitalier de l’Université de MontréalUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsAllianceGeneral partnershipLung transplantationMedicineTransplantationIntensive care medicineGerontologyPolitical scienceSurgery

Abstract

fetched live from OpenAlex

This study employed the James Lind Alliance Priority Setting Partnership methodology to identify and prioritize research priorities in lung transplantation through engagement of pre- and post-lung transplant patients, caregivers, and clinicians in Canada. An initial survey collected 490 questions from 204 respondents, which were collated into 117 summary questions. After removing duplicates and conducting evidence checks, 25 verified uncertainties were discussed at a final workshop, resulting in a consensus-based Top 10 list of research priorities. Key priorities addressed the need to improve immunosuppression regimens, patient education, lung allograft dysfunction, and donor lung availability. This first-of-its-kind initiative in lung transplantation created a stakeholder-driven research agenda that better aligns future research with patient needs. The findings establish a foundation for more patient-centered lung transplantation research.

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)0.001
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 score0.291
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.111
GPT teacher head0.451
Teacher spread0.340 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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