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Modelo de interpretação da tomografia computadorizada de alta resolução do diagnóstico diferencial das doenças intersticiais crônicas

2005· article· pt· W1976598409 on OpenAlexaff
C. Isabela S. Silva, Néstor L. Müller

Bibliographic record

VenueRadiologia Brasileira · 2005
Typearticle
Languagept
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsMedicinePhysicsComputed tomographyRadiology

Abstract

fetched live from OpenAlex

O reconhecimento e o diagnóstico diferencial dos diversos padrões da tomografia computadorizada de alta resolução (TCAR) são de fundamental importância na avaliação das doenças pulmonares difusas. Existem vários padrões de TCAR descritos na literatura, sendo alguns deles superponíveis. A interpretação desses padrões pode ser uma tarefa difícil, particularmente para residentes e radiologistas não especializados em tórax. Os autores ilustram os achados característicos da TCAR nas doenças pulmonares intersticiais crônicas e propõem um modelo simplificado de interpretação desses achados, baseado no padrão e na distribuição da doença. O algoritmo inclui os seis principais padrões de anormalidade identificados na TCAR: septal, reticular, cístico, nodular, de atenuação em vidro fosco e consolidação parenquimatosa.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.318
Teacher spread0.294 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations1
Published2005
Admission routes1
Has abstractyes

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