Construction of a stroke green channel process based on the PDCA cycle management model and its impact on stroke prognosis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of implementing a stroke green channel process (GCP) based on the PDCA (Plan-Do-Check-Act) cycle on stroke prognosis. METHODS: A retrospective analysis was conducted at the Second Affiliated Hospital of Guizhou Medical University by reviewing data of 259 stroke patients from January 2021 to December 2023. Patients were divided into two cohorts: 114 patients managed by the PDCA-based GCP and 145 patients receiving standard care (non-green channel process, NGCP). Key metrics assessed included demographic data, rescue indicators, and prognostic outcomes - neurological function, life ability, and quality of life. RESULTS: The GCP group demonstrated significantly reduced triage (P = 0.009) and computed tomography (CT) scan completion times (P = 0.042), leading to shorter hospital stay durations (P = 0.022) and fewer transfer incidents (P = 0.001). Neurological and cognitive functions improved in the GCP group, evidenced by lower National Institute of Health stroke scale (NIHSS) scores (P = 0.011) and higher Mini-Mental State Examination (MMSE) (P = 0.008) and Montreal Cognitive Assessment (MoCA) scores (P = 0.032). Functional abilities and independence also improved, with higher Activities of Daily Living (ADL) (P = 0.007) and Barthel scores (P = 0.003), alongside lower Modified Rankin Scale (mRS) scores (P < 0.001). Adverse reactions were less frequent in the GCP group (total incidence rate P < 0.001). CONCLUSION: Implementing a stroke GCP managed with the PDCA cycle significantly improves stroke prognosis, enhancing clinical outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".