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Pulmonary Cryptococcosis: CT and Pathologic Findings

2002· article· en· W2335717143 on OpenAlexaff
Steven E. Zinck, Ann N. Leung, Michael Frost, Gerald J. Berry, Néstor L. Müller

Bibliographic record

VenueJournal of Computer Assisted Tomography · 2002
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineHalo signLungCryptococcosisRadiologyPathologyDifferential diagnosisPleural effusionBiopsyRespiratory diseaseRadiologic signSolitary pulmonary noduleComputed tomographyRadiographyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this work was to describe the CT and pathologic findings of pulmonary cryptococcosis. METHOD: CT scans of 11 patients (7 immunocompromised, 4 immunocompetent) with proven pulmonary cryptococcosis were analyzed for number, morphologic characteristics, and distribution of parenchymal abnormalities as well for presence of lymphadenopathy and pleural effusion. Pathology of lung specimens obtained by open biopsy or resection (n = 5) and transbronchial biopsy (n = 1) was reviewed by one dedicated pulmonary pathologist. RESULTS: Pulmonary nodules, either solitary or multiple, were the most common CT finding, present in 10 of 11 patients (91%); associated findings included masses (n = 4), CT halo sign (n = 3), and consolidation (n = 2). On histologic examination, focal areas of ground-glass attenuation surrounding or adjacent to nodules were found to represent airspace collections of macrophages and proteinaceous fluid. CONCLUSION: Pulmonary cryptococcosis should be considered in the differential diagnosis of solitary or multiple pulmonary nodules (with or without associated CT halo sign), particularly in immunocompromised patients.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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 designCase report
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

Citations120
Published2002
Admission routes1
Has abstractyes

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