Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome
Bibliographic record
Abstract
Objective Implement and demonstrate feasibility of in situ simulations to identify latent safety threats (LSTs) at a higher rate than lab-based training, and reinforce teamwork training in a paediatric emergency department (ED). Methods Multidisciplinary healthcare providers responded to critical simulated patients in an urban ED during all shifts. Unannounced in situ simulations were limited to 10 min of simulation and 10 min of debriefing, and were video recorded. A standardised debriefing template was used to assess LSTs. The primary outcome measure was the number and type of LSTs identified during the simulations. Secondary measures included: participants’ assessment of impact on patient care and value to participants. Blinded video review using a modified Anaesthetists Non-Technical Skills scale was used to assess team behaviours. Results 218 healthcare providers responded to 90 in situ simulations conducted over 1 year. A total of 73 LSTs were identified; a rate of one every 1.2 simulations performed. In situ simulations were cancelled at a rate of 28% initially, but the cancellation rate decreased as training matured. Examples of threats identified include malfunctioning equipment and knowledge gaps concerning role responsibilities. 78% of participants rated the simulations as extremely valuable or valuable, while only 5% rated the simulation as having little or no value. Of those responding to a postsimulation survey, 77% reported little or no clinical impact. Video recordings did not indicate changes in non-technical skills during this time. Conclusions In situ simulation is a practical method for the detection of LSTs and to reinforce team training behaviours. Embedding in situ simulation as a routine expectation positively affected operations and the safety climate in a high risk clinical setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".