Bibliographic record
Abstract
Background and aims: whether PLR is equally useful in the critically ill pediatric population is unknown. Aims: To assess whether the passive leg raising test can assist in predicting fluid responsiveness in pediatric patients. Methods: 40 patients admitted to the pediatric intensive care unit aged 1 month to 12.5 years.The continuous non-invasive Cheetah NICOM monitor hemodynamic parameters at intervals. PLR was performed of raising the legs to a 45°angle for 3 minutes. Five minutes after lowering the legs to baseline position, a 10 mL/kg bolus of 0.9% physiologic saline solution was administered within 10 minutes to assess actual fluid responsiveness. The patients were divided into 3 groups by ages, which were age ≤3 years (n=13), 3 < age. Results: the threshold values of an increase in cardiac output of passive leg raising (7.5%-10%) and fluid bolus (12.5%-15%) for responders. a sensitivity of 65% and specificity of 85%. In ≤ 3 year-old children, the acquired value of PLR Threshold and FB Threshold are 10% and 15% with high sensitivity and specificity. In the age of 3 to 6 years children, PLR threshold and FB Threshold value should be selected 5%, to reach a higher sensitivity and specificity. In children ≥ 6 years old, the value of PLR threshold should be selected by 10% and 15% to FB Threshold. Conclusions: Cardiac output changes after PLR can be helpful in predicting fluid expansion in pediatric patients. For obtaining better sensitivity and specificity, different ages may be selected different cutoff values.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.512 | 0.403 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".