MétaCan
Menu
Back to cohort
Record W4240967627 · doi:10.18535/jmscr/v4i1.05

High Resolution Computed Tomography in Interstitial Lung Diseases

2016· article· en· W4240967627 on OpenAlexaff
Dr Santosh Sarudkar

Bibliographic record

VenueJournal of Medical Science And clinical Research · 2016
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging and Pathology Studies
Canadian institutionsASTER
Fundersnot available
KeywordsMedicineComputed tomographyHigh-resolution computed tomographyRadiologyResolution (logic)TomographyInterstitial lung diseaseHigh resolutionNuclear medicineLungInternal medicineArtificial intelligenceRemote sensingGeology

Abstract

fetched live from OpenAlex

The present study was conducted to evaluate the various high resolution computed tomo graphic patterns of interstitial lung diseases, to assess the reversible (active) verses irreversible (fibrotic) interstitial lung disease with follow up examinations and to limit the differential diagnosis and to make the specific diagnosis. Materials and Methods: A total number of 50 patients with suspected or known interstitial lung disease were studied by high resolution multidetector computed tomography (HRCT) over a period of 24 months.

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)0.023
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.007
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 head0.165
GPT teacher head0.541
Teacher spread0.376 · 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 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 routes1
Has abstractyes

Explore more

Same venueJournal of Medical Science And clinical ResearchSame topicMedical Imaging and Pathology StudiesFrench-language works237,207