Diagnostic yield of non‐guided flexible bronchoscopy for peripheral pulmonary neoplasia
Bibliographic record
Abstract
BACKGROUND: The role of conventional bronchoscopy for peripheral pulmonary neoplasia remains controversial. We aimed to assess the diagnostic yield and the added value of non-guided bronchial aspiration, bronchoalveolar lavage (BAL), and brushing for the diagnosis of pulmonary neoplasia not visible endoscopically. METHODS: We retrospectively assessed 207 consecutive patients with a final diagnosis of peripheral lung malignancy who underwent bronchoscopy with non-guided aspiration, brushing, and BAL as their initial evaluation. The influence of clinical and radiological factors on diagnostic yield was assessed using univariate logistic regression analyses. RESULTS: The overall sensitivity of non-guided bronchoscopy was 25.6%, whereas sensitivities for bronchial aspiration, BAL, and brushing were 14.2%, 11.6%, and 16.5%, respectively. Younger age, larger lesion, central/intermediate distance from the hilum, presence of a bronchus sign, and higher standardized uptake value (SUV) on positron emission tomography scan were predictors of a higher diagnostic yield. Conversely, forced expiratory volume in one second, fellow implication in the procedure, and tumor histology did not influence sensitivity. The overall sensitivity of bronshoscopy was >40% for tumors >4 cm, located in the central/intermediate thirds of the lung, showing a bronchus sign, with an SUV >12 or occurring in patients <50 years of age. Conversely, the sensitivity was <10% for tumors <2 cm, located peripherally or with an SUV <4. CONCLUSION: Neoplasia characteristics may help targeting situations in which conventional bronchoscopy could be used as the initial diagnostic procedure when advanced techniques are unavailable. However, advanced diagnostic tools should probably be proposed as the initial modality for the diagnosis of peripheral malignant lesions when available.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".