Nutritional management of cystic fibrosis patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: We critique recent advances in nutritional care of patients with cystic fibrosis focusing on our understanding of the role of fecal elastase as a pancreatic function test, enzyme replacement therapy and bone health. RECENT FINDINGS: Fecal elastase measured by the monoclonal antibody method is a useful screening test for delineating cystic fibrosis patients with pancreatic insufficiency (needing enzyme replacement therapy) from those with pancreatic sufficiency. Cutoff levels, however, have not been clearly defined, particularly in infancy. Newer enzyme products that attempt to improve the intralumenal intestinal pH with bicarbonate or using a combination of unprotected and enteric coated products fail to completely correct nutrient maldigestion. Compromised bone health in cystic fibrosis patients may be due to multiple factors including calcium, vitamin D, and vitamin K deficiencies and lung inflammation. Current recommendations for treatment of bone health in cystic fibrosis are not evidence-based. SUMMARY: Fecal elastase is a useful marker of pancreatic function but limitations for this test exist. The presence of an acidic intestinal milieu and hitherto poorly defined intestinal factors may contribute to the failure of current enzyme preparations to correct nutrient assimilation in cystic fibrosis. The many factors that contribute to bone health must be evaluated when developing treatment strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".