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Record W2947013739 · doi:10.1183/13993003.00787-2019

New understanding in the treatment of cough (NEUROCOUGH) ERS Clinical Research Collaboration: improving care and treatment for patients with cough

2019· editorial· en· W2947013739 on OpenAlexfundno aff
Lorcan McGarvey, Lieven Dupont, Surinder S. Birring, Jeanette Boyd, Kian Fan Chung, Marta Dąbrowska, Christian Domingo, Giovanni Fontana, Laurent Guilleminault, Péter Kardos, Eva Millqvist, Alyn H. Morice, John A. Smith, Jan Willem van den Berg, C. Van De Kerkhove

Bibliographic record

VenueEuropean Respiratory Journal · 2019
Typeeditorial
Languageen
FieldMedicine
TopicRespiratory and Cough-Related Research
Canadian institutionsnot available
FundersManchester Biomedical Research CentreQueen's University BelfastNational Institute for Health and Care ResearchQueen's UniversityEuropean Respiratory Society
KeywordsMedicineChronic coughIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

The NEUROCOUGH ERS Clinical Research Collaboration brings together clinicians, scientists, patients and industry from Europe and beyond, with a focus on improving care and treatment for patients with chronic coughhttp://bit.ly/2Q6l0ER

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)0.074
metaresearch head score (Gemma)0.095
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: Editorial · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.095
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0040.004
Scholarly communication0.0120.013
Open science0.0040.019
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0190.007

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 head0.137
GPT teacher head0.426
Teacher spread0.289 · 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
GenreEditorial

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

Citations21
Published2019
Admission routes1
Has abstractyes

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