The Diagnostic Value of Capillary Refill Time for Detecting Serious Illness in Children: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
IMPORTANCE: Capillary refill time (CRT) is widely recommended as part of the routine assessment of unwell children. OBJECTIVE: To determine the diagnostic value of capillary refill time for a range of serious outcomes in children. METHODS: We searched Medline, Embase and CINAHL from inception to June 2014. We included studies that measured both capillary refill time and a relevant clinical outcome such as mortality, dehydration, meningitis, or other serious illnesses in children aged up to 18 years of age. We screened 1,265 references, of which 24 papers were included in this review. Where sufficient studies were available, we conducted meta-analysis and constructed hierarchical summary ROC curves. RESULTS: Meta-analysis on the relationship between capillary refill time and mortality resulted in sensitivity of 34.6% (95% CI 23.9 to 47.1%), specificity 92.3% (88.6 to 94.8%), positive likelihood ratio 4.49 (3.06 to 6.57), and negative likelihood ratio 0.71 (0.60 to 0.84). Studies of children attending Emergency Departments with vomiting and diarrhea showed that capillary refill time had specificity of 89 to 94% for identifying 5% dehydration, but sensitivity ranged from 0 to 94%. This level of heterogeneity precluded formal meta-analysis of this outcome. Meta-analysis was not possible for other outcomes due to insufficient data, but we found consistently high specificity for a range of outcomes including meningitis, sepsis, admission to hospital, hypoxia, severity of illness and dengue. CONCLUSIONS: Our results show that capillary refill time is a specific sign, indicating that it can be used as a "red-flag": children with prolonged capillary refill time have a four-fold risk of dying compared to children with normal capillary refill time. The low sensitivity means that a normal capillary refill time should not reassure clinicians.
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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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".