Intermittent Proton Pump Inhibitor Therapy in Low-Risk Non-Variceal Upper Gastrointestinal Bleeding May Be Significantly Cost-Saving
Bibliographic record
Abstract
BACKGROUND: High-dose proton pump inhibitor (PPI) therapy, given either intermittently or continuously for non-variceal upper gastrointestinal bleeding (NV-UGIB), is efficacious. Using intermittent PPI for low-risk patients may be cost-saving. Our objective was to estimate the annual cost savings if all low-risk NV-UGIB patients received intermittent PPI therapy. METHODS: Patients who presented to hospital in Calgary, Alberta, who received a PPI for NV-UGIB from July 2015 to March 2017 were identified using ICD-10 codes. Patients were stratified into no endoscopy, high-risk, and low-risk lesion groups and further subdivided into no PPI, oral PPI, intermittent intravenous (IV), and continuous IV subgroups. Average length of stay (LOS) in each subgroup and costs were calculated. RESULTS: We identified 4141 patients with NV-UGIBs, (median age 61, 57.4% male). One-thousand two-hundred and thirty-one low-risk patients received continuous IV PPI, with an average LOS of 6.8 days (95% CI 6.2-7.3) versus 4.9 days (95% CI 3.9-5.9) for intermittent IV patients. If continuous IV PPI patients instead received intermittent IV PPI, 3852 patient days and CAD 11,714,390 (2017 CAD)/year could be saved. CONCLUSIONS: Using real-world administrative data, we demonstrate that a sizable portion of low-risk patients with NV-UGIB who were given continuous IV PPI if switched to intermittent IV therapy could generate significant potential cost savings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".