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Record W2010503456 · doi:10.1097/mcp.0b013e328363f48d

Update on the diagnosis and classification of ILD

2013· review· en· W2010503456 on OpenAlexaff
Christopher J. Ryerson, Harold R. Collard

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineInterstitial lung diseaseClassification schemeSubclinical infectionUsual interstitial pneumoniaIdiopathic interstitial pneumoniaIntensive care medicineInterstitial pneumoniaHypersensitivity pneumonitisIdiopathic pulmonary fibrosisDiseasePathologyLungInternal medicineData science

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this review is to provide an update on the diagnosis and classification of interstitial lung disease (ILD), with a specific focus on newly described ILD subtypes and phenotypes. In addition, the strengths and limitations of the current approach to ILD diagnosis and management are discussed. RECENT FINDINGS: Idiopathic pleuroparenchymal fibroelastosis and acute fibrinous and organizing pneumonia are new entities that have been described in small case series. Undifferentiated connective tissue disease-associated ILD, smoking-related interstitial fibrosis, familial ILD, unclassifiable ILD, and subclinical ILD have also been better characterized in recent publications. New data regarding these conditions are summarized in this review. The multidisciplinary approach to ILD is reviewed, and complementary classification schemes are described that may help direct the management and improve prognostication of some ILDs. SUMMARY: ILDs are a large and heterogeneous group of diseases with several newly characterized subtypes and phenotypes. The current approach to ILD classification has limitations in some patients that can be minimized by considering complementary classification schemes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.179
GPT teacher head0.409
Teacher spread0.230 · 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 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

Citations69
Published2013
Admission routes1
Has abstractyes

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