Comparison of autofluorescence and white-light bronchoscopies performed with the Evis Lucera Spectrum for the detection of bronchial cancers: a meta-analysis
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Bibliographic record
Abstract
BACKGROUND: Many recent studies have reported that autofluorescence bronchoscopy (AFB) has a superior sensitivity and decreased specificity in the diagnosis of bronchial cancers when compared with white-light bronchoscopy (WLB). We specifically analyzed the diagnostic performances of autofluorescence imaging video bronchoscopy (AFI) performed with the Evis Lucera Spectrum from Olympus, which is a relatively novel approach in detecting and delineating bronchial cancers, and compared it to the older WLB method. METHODS: , 2018 for trials in which patients were diagnosed with lung cancer via concurrent or combined use of AFI and WLB. The included studies were required to have a histologic diagnosis as the gold standard comparison, and a sufficient amount of data was extracted to assess the diagnostic capacity. A 2×2 table was constructed, and the area under the receiver-operating characteristic curve (AUC) of AFI and WLB was estimated by using a stochastic model for diagnostic meta-analysis using STATA software. RESULTS: A total of 10 articles were eligible for the meta analysis, comprising 1,830 patients with complete data included in the analysis. AFI showed a superior sensitivity of 0.92 (95% CI, 0.88-0.95) over WLB's 0.70 (95% CI, 0.58-0.80) with P<0.01, and a comparable specificity of 0.67 (95% CI, 0.51-0.80) compared with WLB's 0.78 (95% CI, 0.68-0.86) with P=0.056. Egger's test P value (0.225) demonstrated that there was no publication bias. CONCLUSIONS: Our research showed that in the evaluation of bronchial cancers, AFI was superior to conventional WLB. With its higher sensitivity, AFI could be valuable for avoiding misdiagnosis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it