<i>Effect of Processing on Galactose</i> In Selected Fruits
Bibliographic record
Abstract
PURPOSE: The impact of consuming processed versus fresh fruits and vegetables on the galactose intake of galactosemic patients was compared. METHODS: The galactose content of processed fruits was determined when the following processing methods were used: freezing, drying, blanching, microwaving, canning, and a combination of blanching and freezing. Then three-day food intakes of five subjects with galactosemia were recorded. The records were used to estimate galactose intake, according to previously reported galactose levels for fresh fruits and vegetables and the potential reduction in galactose intake when only processed fruits and vegetables are consumed. RESULTS: The average galactose reduction was approximately 45% for all the fruits and all processing methods, excluding drying. Intakes varied from 17 to 108 mg/day when fresh values were used and 11 to 103 mg/day when only processed fruits and vegetables were consumed. This reduction was statistically significant for four out of five patients. CONCLUSIONS: When the reduction is compared with reported daily fluctuations in galactosemic patients' endogenous galactose production, the clinical significance of reduced free galactose consumption on long-term outcome is unclear. However, metabolic dietitians now have objective data that the processing methods described will lower the free galactose content of the fruits analyzed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".