Feasibility of prehospital delivery of remote ischemic conditioning by emergency medical services in chest pain patients: protocol for a pilot study
Bibliographic record
Abstract
BACKGROUND: Remote ischemic conditioning (RIC) is a non-invasive procedure with hypothesized therapeutic benefits for patients experiencing an acute ST-elevation myocardial infarction (STEMI). Further study of emergency medical services (EMS) delivery of RIC in the prehospital setting is needed to inform the design and methods for future clinical trials of RIC in STEMI patients. The main objective of this pilot study is to assess the feasibility of prehospital delivery of RIC by EMS providers in the United States. METHODS: We will conduct a single-arm study of the standard RIC procedure (i.e., up to 4 cycles of alternating 5-min inflation and 5-min deflation of an upper arm cuff) administered by EMS paramedics in 50 patients experiencing acute onset chest pain. The investigational autoRIC® device (CellAegis Devices, Inc., Toronto, Ontario) will be initiated by paramedics during ground ambulance transport. Automated RIC cycles will continue through emergency department arrival and stay. The primary endpoint will be the completion of all 4 cycles of RIC without interruption. We will also examine study procedures and collect qualitative data from study participants and paramedics. DISCUSSION: To our knowledge, this will be the first study in the United States to assess the feasibility of completing the 40-min RIC procedure when initiated during ground ambulance transport. Findings from this pilot study will be used to optimize the design and methods for a future efficacy trial of RIC in acute STEMI patients. TRIAL REGISTRATION: NCT03400579 (ClinicalTrials.gov). Registered on 17 January 2018.
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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 | 0.059 | 0.039 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".