Systematic Review and Meta-Analysis of Risk Factors for Dehydration and the Development of a Predictive Scoring System
Bibliographic record
Abstract
Background: Dehydration is a prevalent and potentially serious condition, particularly affecting vulnerable populations such as children and older adults. Prompt recognition and intervention are critical for preventing associated complications. Methods: A systematic review and meta-analysis were conducted, registered in PROSPERO (CRD42024594780), to identify key clinical and demographic risk factors associated with dehydration. A comprehensive search of PubMed, Scopus, and the Cochrane Library was performed for studies published between 2000 and 2024. The risk of bias in included studies was assessed using the Newcastle–Ottawa Scale and the Cochrane Risk-of-Bias (RoB) tool. Ten studies met the inclusion criteria for quantitative synthesis. Based on pooled diagnostic metrics, a preliminary scoring tool was developed for dehydration risk stratification. Results: The pooled sensitivity and specificity of common clinical signs, such as thirst, dry mouth, and dark urine, were 85% (95% CI: 80–90%) and 70% (95% CI: 65–75%), respectively. The positive predictive value (PPV) was 75%, and the negative predictive value (NPV) was 80%. Pediatric subgroup analysis yielded the most robust data, while data for adult and elderly populations were limited. A conceptual risk scoring system was proposed based on relative diagnostic utility, though it has not yet been externally validated. Conclusions: Simple clinical signs demonstrate reasonable diagnostic accuracy for identifying individuals at risk of dehydration. The proposed scoring system offers a promising, evidence-informed framework for early risk assessment but requires further validation in prospective studies before integration into clinical practice.
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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 | 0.057 | 0.123 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".