Risks of nutritional deficiencies in young adult type 1 diabetic (T1D) women
Bibliographic record
Abstract
Although nutrition plays a crucial role in the treatment of T1D, there are few studies on the nutritional status. This study aimed to evaluate a target group at risk of nutritional deficiencies. Women with well‐controlled T1D and without renal complications (n=22) and healthy controls (n=27), aged 30‐40 yr‐old, were rigorously selected, having normal body weight and being not pregnant nor vegetarian, among other selection criteria. The two groups were comparable in many aspects. T1D, diagnosed since 21 yrs in average, was monitored by HbA1c level. Serum transferrin ( P<0.02 ) and IGF‐1 concentrations ( P<0.001 ) were reduced in the T1D group. There was no significant difference for albumin. Serum iron concentration was also reduced ( P<0.03 ), although body stores (ferritin) were not affected. Contrarily to other studies, serum Mg and plasma pyridoxal phosphate (PLP, vitamin B6 coenzyme) were not reduced, although alkaline phosphatase, which regulates the concentration of this vitamin, was increased ( P<0.01) . Plasma PLP was below reference value. Erythrocyte folate was greater ( P<0.03 ) in TID group due to a greater number of subjects taking supplements and more control subjects with values in the low range. This study evidenced T1D‐induced metabolic changes in regard to protein and iron status. It also showed a risk of deficiency for vitamin B6.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".