Physician practice and PECARN rule outperform CATCH and CHALICE rules based on the detection of traumatic brain injury as defined by PECARN
Bibliographic record
Abstract
Commentary on : Easter JS, Bakes K, Dhaliwal J, et al. Comparison of PECARN, CATCH, and CHALICE rules for children with minor head injury: a prospective cohort study. Ann Emerg Med 2014;64:145–52.e5.[OpenUrl][1][CrossRef][2][PubMed][3] The recognition of significant traumatic intracranial injuries is important and cranial CT is the gold standard for their diagnosis. However, CT bears risks associated with ionising radiation-induced malignancies, in particular in children. Three high-quality clinical decision rules (CDR) have been developed to assist with decision-making on whether or not to use a cranial CT scan in children who sustain a trauma to the head1: the Canadian Assessment of Tomography for Childhood Head injury (CATCH)2 the Children's Head injury Algorithm for the prediction of Important Clinical Events (CHALICE)3 and the rule developed by the Pediatric Emergency Care Applied Research Network (PECARN).4 They differ significantly in their predictor variables and suggested course of action. They have been derived using different outcomes, inclusion and exclusion criteria and have focused on different severities … [1]: {openurl}?query=rft.jtitle%253DAnn%2BEmerg%2BMed%26rft.volume%253D64%26rft.spage%253D145%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.annemergmed.2014.01.030%26rft_id%253Dinfo%253Apmid%252F24635987%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/j.annemergmed.2014.01.030&link_type=DOI [3]: /lookup/external-ref?access_num=24635987&link_type=MED&atom=%2Febmed%2F20%2F1%2F33.atom
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.290 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".