The use of cognitive screens within major trauma centres in England: A survey of current practice
Bibliographic record
Abstract
Introduction Major trauma centres are effective in reducing morbidity and mortality rates following serious injury. Many patients attending major trauma centres present with traumatic brain injuries, requiring specialist assessment in screening for potential cognitive deficits. Numerous cognitive screens exist but it is currently unclear which are used most frequently within major trauma centres. This study aimed to identify which screening tools are used most frequently in major trauma centres in England to enable discussion around their suitability for this clinical population. Method Electronic surveys were distributed via a mailing list to Clinical Psychologists and Clinical Neuropsychologists in major trauma centres across England to gather data on the use of cognitive screens. Results Fourteen Clinical Psychologists in Neuropsychology participated. Results suggest major trauma centres in England are currently using the ACE-III (50%) or MoCA (42%) as the most frequently used screens for cognitive difficulties following traumatic brain injury. Cognitive screening pathways are multi-disciplinary involving OTs (86%), psychologists (qualified 79%; assistant 57%) psychiatrists (36%), mental health nurses (7%) and therapy assistants (7%). Conclusions Major trauma centres are using evidence-based cognitive screens at present, but further work is needed to develop more effective, better validated cognitive screens for traumatic brain injury populations. Increased inter-professional discussion on the practice of cognitive screening would be beneficial for patients seen within major trauma centres.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".