MétaCan
Menu
Back to cohort

Septal Lines in Pleural Inflammation

2006· article· en· W2019820823 on OpenAlexaff
Jonathan D. Dodd, Carolina A. Souza, Nestor L. M ller

Bibliographic record

VenueJournal of Computer Assisted Tomography · 2006
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineInflammationParenchymaThickeningPleural thickeningLungComputed tomographyRadiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of the study was to evaluate the interstitial changes adjacent to pleural inflammation on multidetector computed tomography. METHODS: The multidetector computed tomography scans of 30 patients with pleural inflammation were retrospectively and blindly evaluated by 2 observers. A control group of 7 patients with documented fibrothorax was also included. The number, appearance, thickness, and extent of septal lines were analyzed. RESULTS: More than 10 septal lines immediately adjacent to the abnormal pleura were seen in 22 of the 30 patients with pleural inflammation and 2 of the 7 patients with fibrothorax (P<0.01). Septal lines that are more than 1 mm thick were seen in 13 of the 30 patients with acute inflammation and none of the patients with fibrothorax (P<0.01). Differences between focal and diffuse pleural inflammation included 10 cm or greater craniocaudal extent and more smooth septal lines with diffuse pleural inflammation. CONCLUSIONS: Pleural inflammation is associated with increased number and thickening of septal lines in the immediately adjacent lung parenchyma.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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 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

Citations1
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Computer Assisted TomographySame topicPleural and Pulmonary DiseasesFrench-language works237,207