Validation of an FFQ to assess dietary protein intake in type 2 diabetic subjects attending primary health-care services in Mali
Bibliographic record
Abstract
OBJECTIVE: To validate a 53-item quantitative FFQ (QFFQ) for the assessment of dietary protein intake in type 2 diabetic outpatients in Bamako, Mali. DESIGN: Consumption of protein-containing foods over the week preceding the interview was measured with a 7d QFFQ and compared with intakes measured with 48-h recalls. SETTING: Centre National de Lutte contre le Diabète. SUBJECTS: Seventeen male and forty female adults with type 2 diabetes. RESULTS: Correlation between protein intakes estimated using the QFFQ and 48h recalls was 0.63 (P < 0.0001). There was no significant difference between the two methods concerning the total protein daily intakes and intakes per kilogram of body weight. The QFFQ indicated that foods of animal origin were a lesser source of protein. Animal protein intake did not differ between men and women but sources did. In men, the main sources were beef (54 % of total animal protein), fish (15 %) and milk powder (8 %). In women, the principal sources were fish (28 %), beef (20 %) and birds (13 %). In contrast, plant protein intake was significantly higher in men than in women (P = 0.01), but the same plant foods contributed in similar proportions for both genders, rice being by far the greatest source (47 % of plant protein in men, 53 % in women). CONCLUSION: The QFFQ developed in this study is a valid tool to evaluate dietary protein intakes in Malian diabetic subjects. While the total protein intakes were low in both men and women, differences in choices and amounts of protein food sources were shown.
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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 | 0.004 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".