Lesion heterogeneity and risk of infectious complications following peripheral endobronchial ultrasound
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The reported incidence of peripheral endobronchial ultrasound (pEBUS)-related infectious complications is below 1%, although studies have never focused solely on them or reported their risk factors. The goal of this study is to describe our local pEBUS infectious complication rate and characterize patient, lesion and procedural factors associated with infectious complications. METHODS: All charts, computed tomography scans and electronic records of patients who underwent a pEBUS at the Foothills Medical Center and South Health Campus Hospital in Calgary between 1 May 2014 and 1 October 2015 were reviewed. RESULTS: One hundred and ninety-nine pEBUS procedures were included in our study. The local infectious complication rate was 4.0% (8/199). Two lesion characteristics were more frequent in patients who suffered infectious complications: larger lesion diameter (P = 0.016) and lesion heterogeneity on imaging suggestive of areas of necrosis (P < 0.001). In a multivariate analysis, only the presence of lesion heterogeneity was significantly associated with infectious complications (OR = 16.74 (2.95-95.08)). The rate of infectious complications in lesions with a heterogeneous appearance was 20.7% (6/29). CONCLUSION: The rate of infectious complications after pEBUS is elevated when biopsying heterogeneous appearing lesions. This may not have previously been reported as studies of pEBUS focused on smaller and probably rarely necrotic lesions. Future studies of methods to prevent infections complications in pEBUS-guided biopsies of heterogeneous appearing lesions are warranted.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".