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Record W2954920114 · doi:10.3390/jcm8070962

Reassessing the Role of Eosinophils as a Biomarker in Chronic Obstructive Pulmonary Disease

2019· review· en· W2954920114 on OpenAlexaff
Mariaenrica Tinè, Davide Biondini, Umberto Semenzato, Erica Bazzan, Manuel G. Cosío, Marina Saetta, Graziella Turato

Bibliographic record

VenueJournal of Clinical Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcGill UniversityChristie (Canada)
FundersUniversità degli Studi di Padova
KeywordsMedicineCOPDExacerbationEosinophilBiomarkerImmunologyImmune systemInternal medicineAsthma

Abstract

fetched live from OpenAlex

Blood eosinophils measurement, as proxy for tissue eosinophils, has become an important biomarker for exacerbation risk and response to inhaled corticosteroids (ICS) in Chronic Obstructive Pulmonary Disease (COPD). Its use to determine the pharmacological approach is recommended in the latest COPD guidelines. The potential role of blood eosinophils is mainly based on data derived from post-hoc and retrospective analyses that showed an association between increased blood eosinophils and risk of exacerbations, as well as mitigation of this risk with ICS. Yet other publications, including studies in real life COPD, do not confirm these assumptions. Moreover, anti-eosinophil therapy targeting interleukin (IL)-5 failed to reduce exacerbations in COPD patients with high blood eosinophils, which casts significant doubts on the role of eosinophils in COPD. Furthermore, a reduction of eosinophils might be harmful since COPD patients with relatively high eosinophils have better pulmonary function, better life quality, less infections and longer survival. These effects are probably linked to the role of eosinophils in the immune response against pathogens. In conclusion, in COPD, high blood eosinophils are widely used as a biomarker for exacerbation risk and response to ICS. However, much is yet to be learned about the reasons for the high eosinophil counts, their variations and their controversial effects on the fate of COPD patients.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.475
Teacher spread0.365 · 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 designOther design
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

Citations25
Published2019
Admission routes1
Has abstractyes

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