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Record W1980133367 · doi:10.1586/ers.12.5

Unifying thoracic biomarkers: surfactant protein-D and beyond

2012· review· en· W1980133367 on OpenAlexaff
Jen Erh Jaw, Don D. Sin

Bibliographic record

VenueExpert Review of Respiratory Medicine · 2012
Typereview
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsCOPDMedicinePulmonary diseasePulmonary surfactantLungBiomarkerIntensive care medicineDiseasePathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Chronic obstructive pulmonary disease (COPD) is a progressive disorder that affects 300 million people worldwide and is responsible for 3 million deaths annually. Currently, there are no accepted biomarkers of COPD, which has impaired drug development and management of patients with COPD. Pneumoproteins, which are proteins synthesized predominantly in the lungs, are promising blood biomarkers because they have high specificity for lung disease. The most promising is surfactant protein-D, which is synthesized largely in Type 2 pneumocytes, and its blood concentrations have been associated with COPD and with certain clinical end points such as mortality. In this paper, we discuss surfactant protein-D and other pneumoproteins as promising biomarkers of COPD.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.002

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 head0.183
GPT teacher head0.497
Teacher spread0.315 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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