Adherence to standard views in skeletal surveys performed on children with known or suspected abuse
Bibliographic record
Abstract
Background and Aim of the Audit:There were 108 child maltreatment cases per 1000 children in 2010 in Canada. The highest rates of investigation involve children < 2 years old. Radiologists play pivotal roles in identifying inflicted injuries. Standardizing the imaging protocol is key to ensuring consistent care and preventing misses, which can have devastating consequences. The ACR and SPR collaboratively set out pediatric SS performance and interpretation practice parameters in 2016. This audit aims to assess adherence to the inclusion of all recommended views in SS performed in patients < 2 years old presenting with known or suspected non-accidental trauma.Audit Target100%Methods100 SS reports for suspected child maltreatment were reviewed. SS for other indications and follow-up surveys were excluded. In accordance with our institutional policy, we also assessed if SS were double-read by two Radiologists. Results Ninety-eight SS were adherent to ACR-SPR guidelines. Ninety-eight SS were double-read. ConclusionsWhile close to meeting the audit target, there is still room for improvement. This audit proved valuable in identifying barriers to adherence.
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 | 0.023 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".