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Record W2775754360 · doi:10.1111/ctr.13168

Utility of transthoracic needle biopsy after lung transplantation

2017· article· en· W2775754360 on OpenAlexaff
John Kavanagh, Miranda Siemienowicz, Shaf Keshavjee, Patrik Rogalla, L.G. Singer, Sonja Kandel

Bibliographic record

VenueClinical Transplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicinePneumothoraxBiopsyLungLung transplantationTransplantationSurgeryLesionRadiologyInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the diagnostic yield and complications of CT-guided transthoracic needle biopsy (TTNB) after lung transplantation. A database search identified all TTNB performed in lung transplant patients over a 14-year period. Forty-two biopsies in transplant patients (transplant group) were identified and matched to the next biopsy performed in native lungs by the same operator (nontransplant group) as a control. Primary outcomes recorded were diagnosis, diagnostic yield, pneumothorax requiring intervention, and symptomatic pulmonary hemorrhage. Biopsy outcomes were classified as diagnostic, not specifically diagnostic, and nondiagnostic. Patients in the transplant group were younger (P < .002). Emphysema along the biopsy trajectory was more commonly seen in the nontransplant group (P < .0006). Needle gauge, size of lesion, pleural punctures, lesion depth, and number of passes were not significantly different. Diagnostic yield was 71% in the transplant group and 91% in the nontransplant group. There were 20 of 42 (48%) malignant nodules in the transplant group compared to 31 of 44 (70%) nodules in the nontransplant group (P = .05). There were no complications in the transplant group. The nontransplant group had two pneumothoraces requiring intervention. TTNB after lung transplant is safe with a moderate diagnostic yield. Nonmalignant lesions are more common after lung transplantation.

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)0.000
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 score0.079
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.102
GPT teacher head0.467
Teacher spread0.365 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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