Endoscopic submucosal dissection for early gastric cancer in elderly patients: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: The effectiveness of endoscopic submucosal dissection (ESD) has been increasingly reported. However, studies addressing the safety and application value of ESD in elderly patients with early gastric cancer (EGC) were still lacking. This meta-analysis was intended to evaluate the feasibility and safety of ESD in elderly patients with EGC. METHODS: A systematic search was conducted in PubMed, EBSCO, Cochrane Library, EMBASE, and Web of Science. Studies were screened out if data of elderly and non-elderly gastric cancer patients were reported separately. The qualities of included studies were assessed using Newcastle-Ottawa Quality Assessment Scale. The pooled odd ratios (ORs) with 95 % confidence intervals (CIs) were calculated. Statistical analysis was conducted using the Review Manager 5.2 (Cochrane Collaboration, Oxford, UK). RESULTS: Nine studies (eight in Japan, one in China), including a total of 30,100 lesions, met the inclusion criteria. The "en bloc" and histological complete resection rates of the elderly and non-elderly groups were similar [OR, 0.98, 95 % CI, 0.56 to 1.71; P = 0.93 and OR, 0.79, 95 % CI, 0.58 to 1.07; P = 0.13, respectively]. As for procedure-related complications, similar perforation rates [OR, 1.19, 95 % CI, 0.94 to 1.51; P = 0.15], and bleeding rates [OR, 1.13, 95 % CI, 0.83 to 1.56); P = 0.43] between the elderly and non-elderly groups were observed. Whereas, the elderly patients had a higher procedure-related pneumonia rate compared with non-elderly ones [OR, 2.18, 95 % CI, 1.55 to 3.08; P < 0.01]. CONCLUSIONS: The ESD procedure appears to be a safe technique in elderly patients with EGC while appropriate approach should be taken to avoid procedure-related pneumonia.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.042 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".