Choosing Wisely Canada: Pediatric Neurosurgery Recommendations
Bibliographic record
Abstract
OBJECTIVES: Choosing Wisely Canada is an evidence-based, patient-focused, physician-led campaign to improve the delivery of medical care in Canada. The goal of this study was to produce Canadian recommendations for physicians treating patients with selected paediatric neurosurgery issues. METHODS: Paediatric neurosurgeons practicing in Canada were invited to participate. Suggestions were obtained using an anonymous questionnaire, and then ranked anonymously by the participating surgeons. Suggestions that received consensus from participants were discussed at the 2016 annual Canadian Pediatric Neurosurgery Study Group meeting. Suggestions that were not evidence based, or that would not have a substantive population impact were eliminated. All remaining suggestions were anonymously ranked by the group and the top five recommendations were submitted to Choosing Wisely Canada. RESULTS: The final five recommendations include: 1) don't order a computed tomography scan to investigate macrocephaly (order an ultrasound or magnetic resonance imaging scan); 2) don't image a midline dimple related to the coccyx in an asymptomatic infant or child; 3) don't use computed tomography scans for routine imaging of children with hydrocephalus. Fast sequence nonsedated magnetic resonance imaging scans or ultrasounds provide adequate information to assess patients without exposing them to radiation or an anesthetic; 4) don't recommend helmets for mild to severe positional flattening; 5) don't do routine surveillance imaging for incidentally discovered Chiari I malformation. CONCLUSIONS: Five Choosing Wisely Canada recommendations were produced to support care of patients with paediatric neurosurgical issues. While these recommendations will apply to the majority of children with the involved conditions, occasionally, deviation from these recommendations may be clinically indicated.
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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 | 0.018 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".