EUS-CGN versus EUS-CPN in pancreatic cancer
Bibliographic record
Abstract
BACKGROUND: Comparison between endosonographic ultrasonography (EUS)-guided celiac ganglia neurolysis (CGN) and EUS-guided celiac plexus neurolysis (CPN) in pain management for pancreatic cancer has engendered controversy. To analyze the effectiveness and safety of EUS-CGN and figure out whether EUS-CGN is better than EUS-CPN, a qualitative systematic review was conducted. METHODS: Studies were searched from Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE up to April 2020. We only included studies with full-text and in English and assessed study quality with Newcastle-Ottawa Scale or Cochrane risk-of-bias tool. We recorded details of study design, participants, procedure performed, protocol of follow-up, pain response, quality of life, survival, and adverse events. The study was conducted under Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement 2009. RESULTS: Five studies involving 319 patients were included. Short-term pain response rates ranged from 65.0% to 88.46% in EUS-CGN group and most studies reported its superiority over EUS-CPN. As for adverse events, the incidence of transient hypotension and gastrointestinal symptoms seemed comparable, while results of initial pain exacerbation varied among studies. Besides, EUS-CGN might provide a shorter survival. CONCLUSION: EUS-CGN can be safely performed while it may shorten survival. In terms of short-term pain response, EUS-CGN is better than EUS-CPN while no conclusion of long-term pain control can be drawn.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".