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Record W2890118318 · doi:10.1001/jamaneurol.2018.2424

Modeling Stroke Patient Transport for All Patients With Suspected Large-Vessel Occlusion

2018· article· en· W2890118318 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJAMA Neurology · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsAlberta Children's HospitalHotchkiss Brain InstituteUniversity of Calgary
FundersAlberta Innovates
KeywordsMedicineThrombolysisTriageOcclusionStroke (engine)GroinSurgeryEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

Importance: Ischemic stroke with large-vessel occlusion can be treated with alteplase and/or endovascular therapy; however, the administration of each treatment is time sensitive. Objective: To identify the optimal triage and transport strategy: direct to the endovascular center (mothership) or immediate alteplase treatment followed by transfer to the endovascular center (drip and ship), for all patients with suspected large-vessel occlusion stroke. Design Setting, and Participants: This was a theoretical, conditional probability modeling study. Existing data from clinical trials of stroke treatment were used for model generation. The study was conducted from February 1, 2017, to March 1, 2018. Main Outcomes and Measures: The time-dependent efficacy of alteplase and endovascular therapy and the accuracy of large-vessel occlusion screening tools were modeled to estimate the probability of positive outcome (modified Rankin Scale score, 0-1 at 90 days) for both the drip-and-ship and mothership transport strategies. Based from onset to treatment, the strategy that estimates the greatest probability of excellent outcome is determined in several different scenarios. Results: The patient's travel time from both thrombolysis and endovascular therapy centers, speed of treatment, and positive predictive value of the screening tool affect whether the drip-and-ship or mothership strategy estimates best outcomes. With optimal treatment times (door-to-needle time: 30 minutes; door-in-door-out time: 50 minutes; door-to-groin-puncture time: 60 minutes [mothership], 30 minutes [drip and ship]), both options estimate similar outcomes when the centers are 60 minutes or less apart. However, with increasing travel time between the 2 centers (90 or 120 minutes), drip and ship is favored if the patient would have to travel past the thrombolysis center to reach the endovascular therapy center or if the patient would arrive outside the alteplase treatment time window in the mothership scenario. Holding other variables constant, if treatment times are slow at the thrombolysis center (door-to-needle time: 60 minutes; door-in-door-out time: 120 minutes), the area where mothership estimates the best outcomes expands, especially when the 2 centers are close together (60 minutes apart or less). The area where mothership estimates the best outcome also expands as the positive predictive value of the screening tool increases. Conclusions and Relevance: This study suggests that decision making for prehospital transport can be modeled using existing clinical trial data and that these models can be dynamically adapted to changing realities. Based on current median treatment times to realize the full benefit of endovascular therapy on a population level, the study findings suggest that delivery of the treatment should be regionally centralized. The study modeling suggests that transport decision making is context specific and the radius of superiority of the transport strategy changes based on treatment times at both centers, transport times, and the triaging tool used.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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