A Canadian Paediatric Surveillance Program study to guide safe integration of virtual care for children
Bibliographic record
Abstract
A 6-year-old previously healthy male presented to the emergency department in shock with a rash and fever. Upon arrival he looked unwell, was febrile (38.8°C), tachycardic (HR 160) and hypotensive (80/60) with petechial rash on his legs. He was also noted to have a markedly enlarged liver and spleen. While being fluid resuscitated, given antibiotics and awaiting bloodwork, his parents reported that he had been unwell for 2 weeks with intermittent fever and progressive rash. They attended a telephone appointment with their paediatrician 1 week prior. They reported the rash and were advised that it was caused by the same virus causing the fever. The laboratory investigation revealed platelets < 1 × 109/L, hemoglobin 65 g/L, and leukocytes 85 × 109/L with 22% blasts. He was stabilized and admitted to the PICU with a presumptive diagnosis of leukemia and septic shock. A 16-year-old female arrived at the emergency department by ambulance with abdominal pain and vomiting. She was accompanied by her mother, who found her in pain in her bedroom when she returned home from work. She reported that her daughter had been diagnosed with depression and anxiety 2 years prior, and had a virtual appointment with her new psychiatrist that morning. Subsequent information revealed that the visit had been difficult and the patient had ended the visit prematurely. The psychiatrist was worried for her safety based on their interaction and had been trying to call her back but was unable to reach her. The secondary number on file for her mother was tried but found to be out of service. It turned out that the phone number on file was off by one digit. The parent brought the child to medical attention the next day when her pain persisted. Bloodwork revealed severe acetaminophen toxicity and liver damage, outside of the window for N-acetyl-cysteine treatment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".