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High-Resolution Computed Tomography Features of Nonspecific Interstitial Pneumonia and Usual Interstitial Pneumonia

2005· article· en· W2091709334 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computer Assisted Tomography · 2005
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHoneycombingMedicineUsual interstitial pneumoniaHigh-resolution computed tomographyRadiologyGround-glass opacityPneumoniaReticular connective tissueDifferential diagnosisPathologyInterstitial lung diseaseLungComputed tomographyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the accuracy of high-resolution computed tomography (HRCT) in the diagnosis of nonspecific interstitial pneumonia (NSIP). We hypothesized that the computed tomography (CT) features of NSIP could be distinguished from those of usual interstitial pneumonia (UIP). METHODS: The HRCT images of 47 patients with surgical lung biopsy-proven NSIP (n = 25) and UIP (n = 22) were independently reviewed by 2 thoracic radiologists. Predominant imaging patterns, most likely diagnosis, and diagnostic level of confidence were recorded. A confident HRCT diagnosis of NSIP was based on the presence of spatially uniform, bilateral, basal-predominant ground-glass and/or reticular opacities with little if any honeycombing, whereas UIP was confidently diagnosed if a spatially inhomogeneous, bilateral, peripheral, basal-predominant pattern of reticular opacities and honeycombing with little if any ground-glass attenuation was identified. RESULTS: A predominant pattern of ground-glass and/or reticular opacity with minimal to no honeycombing was demonstrated in 48 (96%) of 50 readings in patients with NSIP. Conversely, the presence of honeycombing as a predominant feature had a predictive value of 90% for UIP (P < 0.001). Usual interstitial pneumonia was more likely than NSIP to be subpleural and patchy (P < 0.001). A confident CT diagnosis of NSIP and UIP was correct in 73% and 88% of cases, respectively. The correctness of a CT diagnosis made at intermediate or high confidence was 68% and 88%, respectively. The kappa value for distinction of NSIP from UIP was 0.72. CONCLUSION: In contrast to previous reports, NSIP can be separated from UIP in most cases. The presence of honeycombing as a predominant imaging finding is highly specific for UIP and can be used to differentiate it from NSIP, particularly when the distribution is patchy and subpleural predominant. The presence of predominant ground-glass and reticular opacity is highly characteristic of NSIP, but there is a subset of patients with UIP who have this pattern and may require biopsy for differentiation from NSIP.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
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 head0.008
GPT teacher head0.237
Teacher spread0.229 · 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