VALIDITY OF LACTATE CLEARANCE VERSUS PEDIATRIC RISK OF MORTALITY (PRISM) SCORE III AND PEDIATRIC INDEX OF MORTALITY (PIM) SCORE 3 IN PREDICTION OF MORTALITY IN PICU
Bibliographic record
Abstract
Objective: to determine whether lactate clearance in the early period of resuscitation help predict mortality in pediatric intensive care unit and to compare it with the performance of the new risk of mortality (PIM 3) and PRISM III to predict actual mortality among patients in PICU in Al -Azhar University Hospitals (El Hussein &Bab El Sheria). Subjects and Methods: A prospective observational study was conducted on 100 children of both sexes admitted to Pediatric Intensive Care Unit (PICU) of Al -Azhar University Hospitals, pediatric department (El Hussein &Bab El Sheria). They were selected by simple random method. This study was carried out during the period of December 2017 to December 2018. Full history, routine, physical examination and special investigations were taking. Results: Lactate Clearance, PRISM III and PIM 3 score, showed a receiver-operating-characteristics area under the curve (ROC-AUC) value of 0.974, 0.971 and 0.941 respectively to predict PICU mortality (cut-off showing highest sensitivity was 24 hours Lactate Clearance of 15.4%, PIM 3 Score of 33.3, and PRISM III of 16 and specificity was 87.5%, 87.5% and 75% for each respectively. Conclusions: PRISM III and PIM 3 scoring systems shows adequate discriminatory function and well calibrated for the case mix of patients in PICU of Al Azhar University hospitals. It can be used as a beneficial tool for evaluation of risk adjusted mortality. Lactate clearance showed higher sensitivity and specificity than PRISM III and PIM 3 so serum lactate level should be used as one of the first laboratory orders to be measured to all cases admitted at pediatric intensive care unit at admission and after 24 hours followed by calculation of lactate clearance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".