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Record W2796621971 · doi:10.1111/ctr.13233

Quantitative chest <scp>CT</scp> for subtyping chronic lung allograft dysfunction and its association with survival

2018· article· en· W2796621971 on OpenAlexaff
Miho Horie, Pascal Salazar, Tomohito Saito, Matthew Binnie, Kristy K. Brock, Kazuhiro Yasufuku, S. Azad, Shaf Keshavjee, Tereza Martinu, Narinder Paul

Bibliographic record

VenueClinical Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersToshiba Medical Systems
KeywordsBronchiolitis obliteransMedicinePercentileProportional hazards modelLungSurvival analysisHazard ratioPulmonary function testingInternal medicineUnivariate analysisRadiologyLung transplantationMultivariate analysisStatisticsConfidence interval

Abstract

fetched live from OpenAlex

Chronic lung allograft dysfunction (CLAD) is a major cause of mortality in lung transplant recipients. CLAD can be sub-divided into at least 2 subtypes with distinct mortality risk characteristics: restrictive allograft syndrome (RAS), which demonstrates increased overall computed tomography (CT) lung density in contrast with bronchiolitis obliterans syndrome (BOS), which demonstrates reduced overall CT lung density. This study aimed to evaluate a reader-independent quantitative density metric (QDM) derived from CT histograms to associate with CLAD survival. A retrospective study evaluated CT scans corresponding to CLAD onset using pulmonary function tests in 74 patients (23 RAS, 51 BOS). Two different QDM values (QDM1 and QDM2) were calculated using CT lung density histograms. Calculation of QDM1 includes the extreme edges of the histogram. Calculation of QDM2 includes the central region of the histogram. Kaplan-Meier analysis and Cox regression analysis were used for CLAD prognosis. Higher QDM values were significantly associated with decreased survival. The hazard ratio for death was 3.2 times higher at the 75th percentile compared to the 25th percentile using QDM1 in a univariate model. QDM may associate with CLAD patient prognosis.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.169
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.081
GPT teacher head0.406
Teacher spread0.324 · 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.

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

Citations21
Published2018
Admission routes1
Has abstractyes

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