Comparison of autofluorescence and white-light bronchoscopies performed with the Evis Lucera Spectrum for the detection of bronchial cancers: a meta-analysis
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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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.054 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".