Tranexamic acid for acute gastrointestinal bleeding (The HALT-IT trial): Statistical analysis plan for an international, randomised, double blind, placebo-controlled trial
Bibliographic record
Abstract
Abstract Background: Acute gastrointestinal (GI) bleeding is an important cause of mortality worldwide. Bleeding can occur from the upper or lower GI tract, with upper GI bleeding accounting for most cases. The main causes include peptic ulcer / erosive mucosal disease, oesophageal varices and malignancy. The case fatality rate is around 10% for upper GI bleeding and 3% for lower GI bleeding. Rebleeding affects 5-40% of patients and is associated with a four-fold increased risk of death. Tranexamic acid (TXA) decreases bleeding and the need for blood transfusion in surgery and reduces death due to bleeding in patients with trauma and postpartum haemorrhage. It reduces bleeding by inhibiting the breakdown of fibrin clots by plasmin. Due to the methodological weaknesses and small size of the existing trials, the effectiveness and safety of TXA in GI bleeding is uncertain. The HALT-IT trial aims to provide reliable evidence about the effects of TXA in acute upper and lower GI bleeding. Methods: The HALT-IT trial is an international, randomised, double blind (participant and trial staff), placebo-controlled trial of tranexamic acid in 12,000 adults (increased from 8,000) with acute upper or lower GI bleeding. Eligible patients are randomly allocated to receive tranexamic acid (1g loading dose followed by 3g maintenance dose over 24 hours) or matching placebo. The main analysis will compare those randomised to tranexamic acid with those randomised to placebo on an intention-to-treat basis, presenting the results as effect estimates (relative and absolute risks) and confidence intervals. The primary outcome is death due to bleeding within 5 days of randomisation and secondary outcomes are rebleeding, all-cause and cause-specific mortality, thromboembolic events, complications, endoscopic, radiological and surgical interventions, blood transfusion requirements, disability (defined by a measure of patient’s self-care capacity) and number of days spent in intensive care or high dependency units. Subgroup analyses for the primary outcome will consider time to treatment, location of bleeding, cause of bleed and clinical Rockall score. Discussion: We present the statistical analysis of the HALT-IT trial. This plan was published before the treatment allocation was un-blinded.
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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 | 0.049 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".