Comparison of electromagnetic navigation bronchoscopy localization and CT-guided percutaneous localization in resection of lung nodules: A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aimed to evaluate the efficacy and safety between electromagnetic navigational bronchoscopy (ENB) and computed tomography (CT)-guided percutaneous localization before resection of pulmonary nodules. METHODS: Pubmed, Embase, Web of Science, and the Cochrane Library databases were searched from January 1, 2000 to April 30, 2022, for relevant studies. Two reviewers conducted the search, selection, and extraction of data from eligible studies. The risk of bias was assessed using the Newcastle-Ottawa Scale. The primary outcome was the localization success rate, and the secondary outcomes were the pneumothorax incidence and localization time. The meta-analysis was performed by Review Manager 5.4. The protocol for the meta-analysis was registered on PROSPERO (Registration number: CRD42022345972). RESULTS: Five cohort studies comprising 441 patients (ENB group: 185, CT group: 256) were analyzed. Compared with the CT-guided group, the ENB-guided group was associated with lower pneumothorax incidence (relative ratio = 0.16, 95% confidence interval [CI]: 0.04-0.65, P = .01). No significant differences were found in location success rates (relative ratio = 1.01, 95% CI: 0.98-1.05, P = .38) and localization time (mean difference = 0.99, 95% CI: -5.73 to 7.71, P = .77) between the ENB group and CT group. CONCLUSION: Both ENB and CT-guided are valuable technologies in localizing lung nodules before video-assisted thoracoscopic surgery based on current investigations. ENB achieved a lower pneumothorax rate than the CT-guided group. In our opinion, there is no perfect method, and decision-making should be given the actual circumstances of each institute. Future prospective studies in the form of a randomized trial are needed to confirm their clinical value.
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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 | 0.063 | 0.109 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".