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Record W2583564626 · doi:10.1111/resp.13012

Global Strategy for the Diagnosis, Management and Prevention of Chronic Obstructive Lung Disease 2017 Report

2017· article· en· W2583564626 on OpenAlexaff
Claus Vogelmeier, Gerard J. Criner, Fernando J. Martínez, Antonio Anzueto, Peter J. Barnes, Jean Bourbeau, Bartolomé R. Celli, Rongchang Chen, Marc Decramer, Leonardo M. Fabbri, Peter Frith, David Halpin, María Victorina López Varela, Masaharu Nishimura, Nicolás Roche, R Rodríguez-Roisín, Don D. Sin, Dave Singh, Robert A. Stockley, Jørgen Vestbo, Jadwiga A. Wedzicha, Àlvar Agustí

Bibliographic record

VenueRespirology · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsSt. Paul's HospitalUniversity of British ColumbiaMcGill UniversityMcGill University Health Centre
FundersNational Institute for Health and Care Research
KeywordsMedicineIntensive care medicinePulmonary diseaseObstructive lung diseaseDisease managementLungLung diseaseDiseasePathologyInternal medicine

Abstract

fetched live from OpenAlex

This Executive Summary of the Global Strategy for the Diagnosis, Management and Prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017 Report focuses primarily on the revised and novel parts of the document. The most significant changes include: (i) the assessment of chronic obstructive pulmonary disease has been refined to separate the spirometric assessment from symptom evaluation. ABCD groups are now proposed to be derived exclusively from patient symptoms and their history of exacerbations; (ii) for each of the groups A to D, escalation strategies for pharmacological treatments are proposed; (iii) the concept of de-escalation of therapy is introduced in the treatment assessment scheme; (iv)non-pharmacological therapies are comprehensively presented and (v) the importance of co-morbid conditions in managing COPD is reviewed.

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)0.010
metaresearch head score (Gemma)0.014
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0020.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0230.029

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 head0.039
GPT teacher head0.380
Teacher spread0.341 · 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
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

Citations694
Published2017
Admission routes1
Has abstractyes

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