Sz.: Triage of the Child With Abdominal Pain: A Clinical Algorithm for Emergency
Bibliographic record
Abstract
Objective: To create a simplified clinical algorithm for the triage of children with abdominal pain. Design: Data mining methodology (Rough sets analysis) was applied to a randomized data set obtained from patients ' emergency admission charts. Setting: Emergency Room at the Children's Hospital of Eastern Ontario in Ottawa. Population Studied: Retrospective analysis of 175 emergency records. Patients were grouped into 2 categories- those having appendicitis (confirmed by a pathology report), and those discharged from the emergency room and not returning for the same or a related problem. Results: A set of 9 clinical symptoms and signs was identified as being important for patient management. A clinically-based algorithm for the triage of these children is suggested. Conclusions: It is possible to develop a clinical algorithm for triage of abdominal pain that can be used even by non-medical professionals. A template for such an algorithm can be further extended into other pediatric emergencies, such as chest pain, headache, joint pains, etc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".