Utility of transthoracic needle biopsy after lung transplantation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".