Triage of acute abdominal pain in childhood: clinical use of a palm handheld in a pediatric emergency department
Bibliographic record
Abstract
The paper describes design and implementation of a mobile clinical triage support system for the evaluation of acute appendicitis in childhood. The MET (mobile emergency triage) system was developed according to the general principles of client server architecture, with mobile clients running on palm handhelds. Decision model implemented in MET follows the principles of evidence based medicine based on retrospective data. We applied a hybrid methodological approach involving fuzzy measures and rough set theory to develop this model. In a randomized retrospective trial, the triage recommendation of the MET system had a sensitivity of 86.7% and a specificity of 85.7%. MET is a fully functional mobile clinical triage support system that provides triage recommendation at the point of care, irrespective of the completeness of the clinical information. It also allows for data capture and interaction with the hospital's information system. Given mobility of MET and its easy to use features, we are proposing a system that can both support evidence based emergency room patient care, and at the same time, streamline the bedside triage of a child with abdominal pain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".