Diagnostic yield of bronchoalveolar lavage in the investigation of lung cancer
Bibliographic record
Abstract
Intro: Flexible bronchoscopy is routinely used to assess lung cancer and collect specimens, with bronchoalveolar lavage(BAL) estimated to have a diagnostic yield of 43-47% (Detterbeck, FC et al. Chest 2013). However, there is no clear guidance on choosing bronchoscopy as an initial diagnostic test. Therefore, we aimed to identify the factors influencing the pre-pandemic performance of bronchoscopies with BAL when investigating suspicious pulmonary lesions. Methods: We reviewed the charts of 111 patients who underwent bronchoscopy with BAL at our center between January and June 2018 for suspected lung cancer. Efficacy of BAL was assessed for neoplastic diagnosis and subtyping. Results: Mean age of patients was 70(49-90) years, with 59 males. Mean size of lesion was 31mm and 72(64.9%) had a final diagnosis of lung cancer. Of 111 BALs, 20(18%) showed suspicious or cancer cells. Higher diagnostic yield was found with lesions larger than 2cm(27.8% vs 0%, p<0.001), central tumors(41.7% vs 5.8%, p<0.001), bronchus sign(41.7% vs 15.2%, p=0.024) and lymphangitic carcinomatosis(66.7% vs 15.2%, p=0.001). Higher T(p=0.001) with TNM staging system, abnormal endoscopic appearance(p<0.001) and intense positron emission tomography uptake(p=0.010) were other factors linked with better performance. A high malignancy risk score per Mayo Clinic Model was associated with a diagnostic BAL(22.7% vs 3.7% for low-intermediate risk, p=0.084), but not routinely calculated by clinicians in our center. Conclusion: Despite its frequent use in evaluating lung cancer, BAL had a low diagnostic yield of 18% at our center before the COVID-19 era. Several factors affect its efficacy and could guide a more selective approach for future use.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".