Bibliographic record
Abstract
We thank de Koning et al. for their thoughtful letter about our recent publication on emergency department (ED) visits and head computed tomography (CT) utilization from 2006 through 2011. While we do feel strongly that physicians should adhere to evidence-based prediction rules surrounding neuroimaging to minimize ionizing radiation exposure, especially for children and youth, we do acknowledge that there are a variety of age-specific guidelines that may result in corresponding differences in neuroimaging utilization. Often, we hear the adage that “children are not small adults,” but the authors remind us that “adults are not big children.” Specifically, the authors note that the Canadian CT Head Rule and the New Orleans Criteria recommend a head CT scan for all patients above a certain age, and that this may lead to proportionately higher rates of CT scans in the 60+ years population with a head injury when compared to younger patients. However, from the data set used for our analyses, it is unclear how an actual increased incidence of head injuries, a change in the patterns of ICD codes used, or other factors relatively contributed to the increase in final diagnosis of concussion in this older population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.026 | 0.031 |
| Insufficient payload (model declined to judge) | 0.018 | 0.015 |
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".