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Record W3133722772 · doi:10.1126/sciadv.abd9436

PD-L1 <sup>+</sup> neutrophils contribute to injury-induced infection susceptibility

2021· article· en· W3133722772 on OpenAlexaff
Ajitha Thanabalasuriar, Abby J. Chiang, Christopher Morehouse, Margarita Camara, Shonda Hawkins, Ashley Keller, Adem C. Koksal, Carolina Caceres, Aaron A. Berlin, Nicholas Holoweckyj, Virginia Takahashi, Lily Cheng, Melissa de los Reyes, Mark Pelletier, Andriani C. Patera, Bret R. Sellman, Sonja Hess, Marcello Marelli, Chelsea C. Boo, Taylor S. Cohen, Antonio DiGiandomenico

Bibliographic record

VenueScience Advances · 2021
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsMcGill University
FundersAstraZeneca
KeywordsImmunologyWound infectionMicrobiologyBiologyMedicineSurgery

Abstract

fetched live from OpenAlex

neutrophil accumulation in the lung. Systemic treatment of injured animals with an anti-PD-L1 antibody prevented neutrophil accumulation in the lung and reduced susceptibility to infection but augmented skin healing, resulting in increased epidermal growth. This work provides evidence that injury promotes changes to neutrophils that are important for wound healing but contribute to infection susceptibility.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.023
GPT teacher head0.353
Teacher spread0.330 · 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 designBench or experimental
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

Citations46
Published2021
Admission routes1
Has abstractyes

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