Effectiveness and safety of endoscopic submucosal dissection for residual or recurrent colorectal neoplasia: Meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Endoscopic submucosal dissection (ESD) is a potentially surgery-sparing technique for colorectal neoplasia resection. Outcomes of ESD for residual or recurrent colorectal neoplasia are not well described. This meta-analysis aimed to evaluate the effectiveness and safety of ESD in treating residual or recurrent colorectal neoplasia. We searched MEDLINE and Embase up to July 24, 2023 for studies on ESD for residual or recurrent colorectal neoplasia at prior surgery or endoscopic resection sites. The primary outcome of the meta-analysis was R0 resection; secondary outcomes included recurrence, adverse events (AEs), procedure time, and hospitalization length. Pooled effect sizes were obtained using inverse variance random effects models. Subgroup analyses were based on study location, lesion size, and endoscopist experience. From 1,133 abstracts, data from 25 observational studies were included, reporting on 863 residual or recurrent lesions treated with ESD. R0 resection was achieved in 80.7% of patients (95% confidence interval 72.7–86.7%, I2 = 81%) of patients, whereas recurrence occurred in 2.0% (0.7–5.1%, I2 = 0%). Incidence of delayed bleeding and delayed perforation were 1.8% (0.7–4.2%, I2 = 0%) and 1.9% (0.6–6.3%, I2 = 35%), respectively. The former was independent of country of study, recurrent lesion size, or endoscopist experience. Mean procedure duration was 80.4 minutes (66.6–94.2, I2 = 96%) and hospitalization length was 4.2 days (2.0–6.4, I2 = 98%). This meta-analysis suggests that ESD is effective and safe for treating residual or recurrent colorectal neoplasia after previous resection, with further prospective validation studies needed to compare ESD with other endoscopic resection methods and surgery in this context.
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.
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) | 0.001 | 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) | 0.001 | 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