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Record W2962085649 · doi:10.1186/s12931-019-1108-9

Weighing the evidence for pharmacological treatment interventions in mild COPD; a narrative perspective

2019· review· en· W2962085649 on OpenAlexaff
Dave Singh, Anthony D’Urzo, James F. Donohue, Edward Kerwin

Bibliographic record

VenueRespiratory Research · 2019
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of Toronto
FundersManchester Biomedical Research CentreNational Institute for Health and Care ResearchAstraZeneca
KeywordsCOPDMedicineIntensive care medicineDiseaseSpirometryPsychological interventionNarrative reviewClinical trialPhysical therapyRandomized controlled trialObstructive lung diseaseInternal medicineAsthmaPsychiatry

Abstract

fetched live from OpenAlex

There is increasing focus on understanding the nature of chronic obstructive pulmonary disease (COPD) during the earlier stages. Mild COPD (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stage 1 or the now-withdrawn GOLD stage 0) represents an early stage of COPD that may progress to more severe disease. This review summarises the disease burden of patients with mild COPD and discusses the evidence for treatment intervention in this subgroup.Overall, patients with mild COPD suffer a substantial disease burden that includes persistent or potentially debilitating symptoms, increased risk of exacerbations, increased healthcare utilisation, reduced exercise tolerance and physical activity, and a higher rate of lung function decline versus controls. However, the evidence for treatment efficacy in these patients is limited due to their frequent exclusion from clinical trials. Careful assessment of disease burden and the rate of disease progression in individual patients, rather than a reliance on spirometry data, may identify patients who could benefit from earlier treatment intervention.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.644
GPT teacher head0.606
Teacher spread0.038 · 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.

Study designNot applicable
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

Citations24
Published2019
Admission routes1
Has abstractyes

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