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Record W2915596936 · doi:10.1186/s41077-018-0066-5

Selected abstracts from the 24th Annual Meeting of the Society in Europe for the Simulation Applied to Medicine

2018· article· en· W2915596936 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvances in Simulation · 2018
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsnot available
FundersQueen's UniversityHospital for Sick ChildrenQueen's University Belfast
KeywordsProcess (computing)Clinical Practice3d modelMedicineMedical educationComputer scienceArtificial intelligenceNursing

Abstract

fetched live from OpenAlex

Ethics statement:
\nThe authors declare that they have followed the guidelines for scientific integrity and professional ethics. The article does not contain any studies with human or animal subjects.
\n
\nIntroduction & Aim: 
\nStroke is one of the leading causes of morbidity and mortality worldwide. In eligible patients with acute ischemic stroke, early treatment with intravenous thrombolysis is crucial for a good patient outcome. We introduced simulation training sessions in conjunction with an improved treatment protocol as part of a quality improvement project to reduce door-to-needle times in stroke thrombolysis.
\n
\nMethods:
\nA questionnaire assessing our preexisting treatment protocol was sent to all members of the stroke team. A panel of experts reviewed the responses and suggested potential changes to streamline the treatment protocol. In February 2017, we introduced the new protocol along with weekly videotaped in-situ scenario based simulation sessions with all stroke team members as participants. Previous stroke patients acted as markers. Kirkpatrick’s four-level training evaluation model was used for assessment. Here we present 1) Participant reactions (level 1) on a Likert item from 0-10, and 2) Median door-to-needle times in stroke thrombolysis, a measure of clinical behavioral change (level 3), using a statistical process control method. Simulated performance and long term patient outcomes will be assessed in future analysis.
\n
\nResults & Discussion:
\nParticipant reactions were predominantly positive. Self-perceived learning scored a median of 8 (IQR 7-9). We compared door-to-needle times for 478 prospectively included patients with acute ischemic stroke treated with intravenous thrombolysis at our hospital from January 2014 – July 2017. There was a significant reduction in median door-to-needle time from 27 (IQR 19-41) to 13 minutes (IQR 9-21, p<0.001) for the 78 patients in the post-intervention group. The results remained significant regardless of time of admission. There were no significant changes in the rate of stroke mimics, prehospital time or fatal intracranial hemorrhage.
\n
\nSimulation training in conjunction with protocol improvement led to an immediate and significant reduction of median door-to-needle time in stroke thrombolysis (Fig. 1). To our knowledge, no other published data have shown lower median treatment times. Combining simulation training with protocol change holds promise as a method both for effective implementation and significant results in attempts to reduce in-hospital delays in stroke thrombolysis. Effects on non-technical skills, provider variability and long term patient outcomes are yet to be evaluated.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.337
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it