Relevance of Assessment Methods for Fluid Intake
Bibliographic record
Abstract
Reliable data at population level are essential to firmly establish links between fluid intake, hydration and health, investigate dose-response relationships and develop meaningful public health strategies or reference intake values. However, limited research exists regarding the most appropriate methodology for assessing beverage or total fluid intake (TFI). To date, methodologies have been developed to assess food and nutrient intake without due consideration of water or fluid intake behavior. A recent crossover study showed that a 24-hour food recall significantly underestimated mean TFI by 382 ml (95% CI 299-465) compared with a fluid specific 7-day record. The authors postulated that this average difference was mainly the result of missed drinking acts between meals a 24-hour recall was used. Using a 7-day record administered in paper form or on-line has also been shown to lead to a significantly different mean TFI of 129 ml. Therefore, the choice of methodology might result in measurement errors that limit between-survey or between-country comparisons. Such errors may contribute to variations in estimates of TFI that cannot be explained by differences in climate, physical activity or cultural habits. A recent survey confirmed the variation in methodologies used in European national dietary surveys. Since these surveys form the basis for setting adequate intakes for total water intake, measurement error between surveys should be limited, highlighting the need for the development of a consistent methodology that is validated for water and TFI estimation.
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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 | 0.230 | 0.627 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".