Bronchoalveolar Lavage and Lung Biopsy in Patients With Cancer and Hematopoietic Stem-Cell Transplantation Recipients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: The objective of this study was to describe the diagnostic yield and complication rate of bronchoalveolar lavage (BAL) and lung biopsy in the evaluation of pulmonary lesions in patients with cancer and recipients of hematopoietic stem-cell transplantation (HSCT). METHODS: We conducted a systematic literature review and performed electronic searches of Ovid MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials. Studies were included if patients had cancer or were recipients of HSCT, and if they underwent BAL or lung biopsy for the evaluation of pulmonary lesions. Only English language publications were included. RESULTS: In all, 14,148 studies were screened; 72 studies of BAL and 31 of lung biopsy were included. The proportion of procedures leading to any diagnosis was similar by procedure type (0.53 v 0.54; P = .94) but an infectious diagnosis was more common with BAL compared with lung biopsy (0.49 v 0.34; P < .001). Lung biopsy more commonly led to a noninfectious diagnosis (0.43 v 0.07; P < .001) and was more likely to change how the patient was managed (0.48 v 0.31; P = .002) compared with BAL. However, complications were more common with lung biopsy (0.15 v 0.08; P = .006), and procedure-related mortality was four-fold higher for lung biopsy (0.0078) compared with BAL (0.0018). CONCLUSION: BAL may be the preferred diagnostic modality for the evaluation of potentially infectious pulmonary lesions because of lower complication and mortality rates; thus, choice of procedure depends on clinical suspicion of infection. Guidelines to promote consistency in the approach to the evaluation of lung infiltrates may improve clinical care of patients.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".