Analgesia Administration for Acute Abdominal Pain in the Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVE: To document the use of analgesia for children with acute abdominal pain in the Pediatric Emergency Department (PED) and to compare between children with suspected appendicitis in a high versus low probability. STUDY DESIGN: Patients 0-16 years recruited prospectively as part of another PED study in Toronto. History of present illness and physical examination was available, and information on analgesia administered in the PED was retrospectively collected from charts. Physicians' probability of appendicitis before any imaging was recorded. A follow-up call was made to verify final diagnosis. RESULTS: We included 438 patients, 16% with appendicitis. Analgesics were given 154 times to 112 patients. Thirty-one percent of the cohort received analgesia before seeing the physician, mostly febrile, 37% after seeing the physician, and 17% after seeing a pediatric-surgery consultant. Fifteen percent received multiple dosages. Underdosing was recorded in 14% of medications, mostly morphine (24%). Analgesia was given significantly more often to children with high probability of appendicitis. Age was not a factor in analgesia administration. CONCLUSION: Children with abdominal pain receive more analgesia when the physician suspects appendicitis, yet only in half of the cases, and only 15% receive opioids. Opioid underdosing happens in a quarter of times it is given.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".