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Record W4411672318 · doi:10.1164/rccm.202506-1352st

Addressing Knowledge Gaps in the Early Detection of Bronchiolitis Obliterans Syndrome after Hematopoietic Cell Transplantation: An Official American Thoracic Society Research Statement

2025· article· en· W4411672318 on OpenAlexaff
Guang‐Shing Cheng, Ajay Sheshadri, Kirsten M. Williams, Joe L. Hsu, Thomas Agoritsas, Maryan Ali, Louise Bondeelle, Guy Bouguet, Pascal Chanez, Kenneth R. Cooke, Craig J. Galbán, Samuel Goldfarb, Teal S. Hallstrand, Sarah Johnson, David Lam, David Michonneau, David N. O’Dwyer, Sophie Paczesny, Husham Sharifi, Jamie L. Todd, Daniel Wolff, Hemang Yadav, Gregory A. Yanik, Anne Bergeron

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsMcMaster University
FundersNIH Clinical CenterNational Heart, Lung, and Blood InstituteNational Center for Advancing Translational SciencesDeutsche Forschungsgemeinschaft
KeywordsMedicineBronchiolitis obliteransHematopoietic stem cell transplantationStatement (logic)BronchiolitisTransplantationIntensive care medicineHematopoietic cellLung transplantationHaematopoiesisImmunologyInternal medicineStem cell

Abstract

fetched live from OpenAlex

Abstract Background Bronchiolitis obliterans syndrome (BOS) is a late-onset noninfectious pulmonary complication of allogeneic hematopoietic cell transplant (HCT) that is often diagnosed at an advanced stage with severe lung impairment. Increasing use of HCT for the treatment of hematologic diseases worldwide translates to an increasing burden of BOS, particularly for the community pulmonologist. Early recognition of BOS, which offers the best opportunity to mitigate morbidity and mortality, is hampered by incomplete knowledge of the clinical course and disease process. The goal of this research statement is to survey our current understanding of BOS and to define the research agenda for the early detection of BOS. Methods We convened a multidisciplinary panel that included community representatives for an in-depth survey of the published literature followed by an online workshop. Results Major knowledge gaps were identified within interrelated themes of natural history and pathogenesis, risk factors, and the clinical diagnostic approach. Conclusions This statement reflects the detailed assessment of identified knowledge gaps with associated key research questions, as well as a proposed research road map to stimulate cross-disciplinary collaborations from preclinical to clinical investigations.

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)0.174
metaresearch head score (Gemma)0.234
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.174
Threshold uncertainty score0.920

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1740.234
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.005
Science and technology studies0.0030.004
Scholarly communication0.0090.010
Open science0.0030.007
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.069
GPT teacher head0.450
Teacher spread0.382 · 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
GenreCommentary

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

Citations8
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Respiratory and Critical Care Medicine→Same topicTransplantation: Methods and Outcomes→French-language works237,207→