Bibliographic record
Abstract
Purpose: Proton Pomp Inhibitors (PPIs) are widely prescribed drugs in an hospital setting. Many reviews denounce an overuse of these medications. Our hypothesis is that PPI use correlates with disease severity in hospitalized patients. Methods: We undertook a retrospective cohort study of the patients hospitalized at the Centre Hospitalier Universitaire de Sherbrooke (Quebec) between January 1st 2003, and June 30th, 2004. Sex, age, length of stay in hospital, PPI use, and death information was collected from 7420 episodes of care, corresponding to 5619 patients. Comorbidities were noted and the severity graded according to the Charlson score (18 criterias). Data was analysed using Cox regression. Results: 3134 hospitalized patients (42.2%) received PPIs during their hospital stay, of those 43.3% were males, and 56.7% were females. Of the patients on PPIs, 852 (27.2%) were aged 18–64, while 2282 (72.8%) were 65 and over (p = 0.00). The Charlson score was analysed using 4 subgroups (group 1 = no comorbidity, group 2 = 1–3 comorbidities, group 3 = 4–6 comorbidities, group 4 = 7 or more comorbidities). 329 pts (20.9%) were taking PPIs in group 1, compared to 1457 (42.5%) in group 2, 955 (54.2%) in group 3, and 393 (59.5%) in group 4 (p = 0.00). PPIs were prescribed to 439 pts (24.6%) hospitalized for 1–3 days, while they were prescribed to 783 pts (38.4%) hospitalized for 4–7 days, to 728 pts (45.3%) staying for 8–14 days, and to 1184 pts (59.5%) hospitalized for 15 days or more (p = 0.00). Of the pts who died during the study period, 633 (54.6%) were taking PPIs, compared to a 40% use in pts still alive (p = 0.00). Conclusions: PPI use is related to advanced age, increased comorbidities (higher Charlson score), longer hospitalizations, and higher risk of death. Perhaps our study was not meant to evaluate the misuse of PPIs, our data mostly indicates that PPI use is a reflect of the burden of disease of hospitalised patients, and thus could be a sign of poorer prognosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".