A Novel Imaging Technique to Measure Capillary-Refill Time: Improving Diagnostic Accuracy for Dehydration in Young Children With Gastroenteritis
Bibliographic record
Abstract
BACKGROUND: Assessment of dehydration in young children currently depends on clinical judgment, which is relatively inaccurate. By using digital videography, we developed a way to assess capillary-refill time more objectively. OBJECTIVE: Our goal was to determine whether digitally measured capillary-refill time assesses the presence of significant dehydration (> or = 5%) in young children with gastroenteritis more accurately than conventional capillary refill and overall clinical assessment. METHODS: We prospectively enrolled children with gastroenteritis, 1 month to 5 years of age, who were evaluated in a tertiary-care pediatric emergency department and judged by a triage nurse to be at least mildly dehydrated. Before any treatment, we measured the weight and digitally measured capillary-refill time of these children. Pediatric emergency physicians determined capillary-refill time by using conventional methods and degree of dehydration by overall clinical assessment by using a 7-point Likert scale. Postillness weight gain was used to estimate fluid deficit; beginning 48 hours after assessment, children were reweighed every 24 hours until 2 sequential weights differed by no more than 2%. We compared the accuracy of digitally measured capillary-refill time with conventional capillary refill and overall clinical assessment by determining sensitivities, specificities, likelihood ratios, and area under the receiver operator characteristic curves. RESULTS: A total of 83 patients were enrolled and had complete follow-up; 13 of these patients had significant dehydration (> or = 5% of body weight). The area under the receiver operator characteristic curves for digitally measured capillary-refill time and overall clinical assessment relative to fluid deficit (< 5% vs > or = 5%) were 0.99 and 0.88, respectively. Positive likelihood ratios were 11.7 for digitally measured capillary-refill time, 4.5 for conventional capillary refill, and 4.1 for overall clinical assessment. CONCLUSIONS: Results of this prospective cohort study suggest that digitally measured capillary-refill time more accurately predicts significant dehydration (> or = 5%) in young children with gastroenteritis than overall clinical assessment.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".