Triage of the child with abdominal pain: A clinical algorithm for emergency patient management
Bibliographic record
Abstract
OBJECTIVE: To create a simplified clinical algorithm for the triage of children with abdominal pain. DESIGN: Retrospective analysis. SETTING: Emergency room at the Children's Hospital of Eastern Ontario, Ottawa, Ontario. METHODS: A data mining methodology (rough sets analysis) was applied to a randomized data set obtained from 175 emergency room admission charts of patients. Patients were placed into two diagnostic decision classes: appendicitis confirmed by a pathological report, and resolution (this classification implied the resolution of all clinical complaints and physical findings, with no pathological diagnosis and no operative procedure). RESULTS: Nine clinical symptoms and signs were identified as being important in the management of children with abdominal pain. A clinically based algorithm for the triage of such children was developed. CONCLUSIONS: It is possible to develop a clinical algorithm for the triage of children with abdominal pain that can also be used by nonmedical professionals. A template for such an algorithm can be used as the basis for diagnosing other paediatric emergencies, such as chest pain, headaches and joint pain.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".