Dose optimization of pancreatic enzyme replacement therapy is essential to mitigate muscle loss in patients with advanced pancreatic cancer and exocrine pancreatic insufficiency
Bibliographic record
Abstract
BACKGROUND & AIMS: Exocrine pancreatic insufficiency (EPI) contributes to malnutrition, marked by muscle loss during chemotherapy for advanced pancreatic cancer (aPC). Pancreatic enzyme replacement therapy (PERT) is recommended for patients with EPI; however, it's efficacy for attenuating muscle loss has not been demonstrated. We aimed to delineate the impact of PERT dose on muscle loss using a 7-year population-based cohort with aPC who were provided PERT at the discretion of their oncologist or dietitian according to clinical indications of EPI. METHODS: ). Clinical and pharmaceutical data were retrieved from provincial registries. For patients who were dispensed PERT -8 to +6 weeks from chemo start (PERT users), estimated dose consumed per day was calculated as: (total dose dispensed) / (days, first to last dispensation). PERT users were categorized as high dose or low dose users according to the median estimated dose consumed. Non-users were classified as No PERT. Association between PERT use and muscle loss was analyzed with multivariable logistic regression. RESULTS: Among 210 patients, 81 (39%) were PERT users. Median estimated dose consumed per day of 75 000 USP lipase units defined the cutoff between low dose and high dose uses. There were no significant differences in baseline characteristics between high dose and low dose groups. Muscle loss was more prevalent among low dose compared to both high dose and No PERT groups (88% vs. 58% and 67%, p < 0.05). In the multivariable model predicting muscle loss, low dose PERT was independently associated with greater odds of muscle loss (OR 5.4, p = 0.004) vs. high dose, independent of tumour response, disease stage, and chemotherapy regimen. CONCLUSION: In patients with clinical indications of EPI during chemotherapy for aPC, low doses of PERT were insufficient to prevent muscle loss. Patients with EPI consuming higher doses of PERT had similar odds of muscle maintenance to patients without clinical indications of EPI. Provider education for optimal PERT dosing in patients with EPI should be prioritized, and resources must be allocated to support dose titration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".