Motion – Prophylactic Banding of Esophageal Varices Is Useful: Arguments for the Motion
Bibliographic record
Abstract
Variceal hemorrhage is a frequent complication of cirrhosis and is associated with a high mortality rate, especially in patients with decompensated liver disease. Endoscopy is useful in identifying factors that predict a high likelihood of bleeding, including large varices and red colour signs. Endoscopic rubber band ligation has superseded sclerotherapy in the prevention of both recurrent hemorrhage and the first episode of bleeding, because it causes fewer complications and requires fewer sessions to eradicate varices. It has been proven to be more effective than nontreatment in the primary prophylaxis against variceal hemorrhage. There is extensive literature that has found that band ligation is more effective than beta-adrenergic receptor antagonists at preventing the first variceal hemorrhage. There is ongoing debate about the relative merits of these two approaches, but the available evidence supports the conclusion that band ligation is the treatment of choice in the primary prevention of variceal bleeding. Trials of combined medical and endoscopic therapy are eagerly awaited, and the author suspects that it may prove to be more effective than either modality alone.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".