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Record W2560123122 · doi:10.1093/qjmed/hcw119.021

P017 <break /> Clinical Characteristics of Interstitial Lung Disease Patients: Report from a Single Center Longitudinal Database

2016· article· en· W2560123122 on OpenAlexaffabout
Chelsea Ford-Sahibzada, Kerri A. Johannson, G.C. Goobie, Charlene D. Fell

Bibliographic record

VenueQJM · 2016
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSingle CenterInterstitial lung diseaseDatabaseMedicineLongitudinal dataCenter (category theory)LungComputer scienceInternal medicineData miningChemistry

Abstract

fetched live from OpenAlex

Background: The interstitial lung diseases (ILDs) are a large group of disorders with heterogeneous clinical presentations and outcomes. The University of Calgary longitudinal ILD database was established to characterize the clinical characteristics and outcomes of ILD patients from a single center. Methods: Consecutive patients were prospectively enrolled in the longitudinal database between 2007 and 2013, with data collected up to May 2016. Baseline demographic features and autoimmune serology plus longitudinal lung function, 6-minute walk test and survival data were collected over follow-up time. Descriptive statistics were used to characterize the cohort. Results: Of 198 patients enrolled, 187 with complete records were included in this analysis with a mean follow-up duration of 4.7 (±2.6) years (Table). The mean age was 65 (±12.6) years with 56% of the cohort being female. Mean baseline forced vital capacity was 78% predicted (±20.9) and mean diffusion capacity of the lung for carbon monoxide was 60.3% predicted (±21.2). Eighty-one patients (43.3%) died and 5 (2.7%) underwent lung transplantation. Of the 187 patients, 79 (42.3%) had idiopathic pulmonary fibrosis, 58 (31.0%) had connective tissue-associated ILD, and 48 (25.67%) were unclassifiable. Of these, the differential diagnosis for 31 patients (65%) included idiopathic non-specific interstitial pneumonia vs. IPF.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.299
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2016
Admission routes2
Has abstractyes

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