Endoscopic ultrasound versus computed tomography in determining the resectability of pancreatic cancer: A diagnostic test accuracy meta-analysis
Bibliographic record
Abstract
BACKGROUND/AIM: Endoscopic ultrasound (EUS) and contrast-enhanced computed tomography (CT) with pancreas protocol are used in assessing the resectability of neoplastic pancreatic lesions. Here, we performed a diagnostic test accuracy (DTA) meta-analysis, comparing the diagnostic accuracy of EUS and CT in evaluating the resectability of pancreatic cancer using surgical assessment as the reference standard. PATIENTS AND METHODS: A comprehensive electronic search was conducted up to March 2020. Studies comparing EUS and CT in assessing the resectability of pancreatic cancer using surgical assessment as reference standard were included. QUADAS-2 tool was used to assess the quality of the included studies. After data extraction, an analysis was done using DerSimonian Laird method (random-effects model) to estimate the overall diagnostic odds ratio (DOR) and determine the best-fitting receiver operating characteristics (ROC) curve. RESULTS: Two studies, with 77 subjects combined, were included in the analysis. Overall, the risk of bias was moderate. EUS and CT were comparable in determining the resectability of pancreatic cancer with AUC = 75% (95% confidence interval (CI) 66%- 84%) for EUS as compared to 78% (95% CI 69%- 87%) for CT (P > 0.05). Pooled sensitivity and specificity was 87% (95% CI 70%- 96%) and 63% (95% CI 48%- 77%), respectively for EUS and 87% (95% CI 70%- 96%) and 70% (95% CI 55%- 83%), respectively for CT. DOR was 11.51 (95% CI 3.55- 36.81) for EUS as compared to 15.91 (95% CI 4.83- 51.62) for CT (P > 0.05). CONCLUSIONS: Both EUS and CT provide reasonable sensitivity and specificity to detect the resectability of pancreatic cancer.
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.
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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.055 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".