The clinical, economic, and humanistic burden of treatments for exocrine pancreatic insufficiency and cost-effectiveness of treatments: A systematic literature review
Bibliographic record
Abstract
BACKGROUND: To examine the burden of exocrine pancreatic insufficiency (EPI), specifically the clinical impact of EPI on patients, their quality of life (QoL) and the cost-effectiveness of existing treatments. METHODS: A systematic literature review was conducted using key search terms for the clinical, economic, and humanistic burden. Databases were searched from 2010 to 2022, with articles screened independently by 2 reviewers at abstract and full-text stage against pre-defined eligibility criteria. RESULTS: Seventy-one publications were identified that reported relevant clinical, humanistic, and economic data. Prevalence and incidence of EPI varied across identified studies; EPI appears to be especially prevalent as a comorbid condition in patients with cystic fibrosis. EPI has a large impact on QoL, with lower QoL scores in patients with EPI compared with those without EPI. The instruments used to assess QoL, however, were inconsistent across studies. Where reported, economic burden studies highlighted that patients with EPI have higher healthcare resource utilization compared with those without, with costs increasing with disease severity. CONCLUSION: This systematic literature review highlights that patients with EPI have higher treatment costs and lower QoL scores than patients without EPI. The prevalence of EPI as a comorbid condition is high, particularly in patients with cystic fibrosis.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".