Abstract NS23: Changing State-wide Stroke Practice: The QASC Implementation Project
Bibliographic record
Abstract
Background: The Quality in Acute Stroke Care (QASC) Trial 1 determined that a multidisciplinary supported, nurse-initiated, evidence-based intervention involving supported implementation of clinical protocols to manage fever, hyperglycaemia and swallowing (FeSS protocols) following stroke decreased death and dependency by 16% (p=0.002); reduced temperatures (p=0.001) and glucose levels (p=0.02); and improved swallowing management (p=<0.001). Yet, upscale and spread of even proven interventions on a state-wide level is challenging. Aim: To implement the FeSS protocols from the QASC Trial in all 36 stroke services in NSW, Australia. Method: Our 14 month translational project replicated the intervention from the original QASC Trial. We conducted barrier and enabler assessments and an educational workshop, engaged local opinion leaders, used reminders, and provided ongoing site champion support. Participating sites audited 40 pre-, and 40 post- implementation medical records using the National Stroke Foundation clinical audit web-based tool. Results: All (n=36, 100%) sites participated in the medical record audit (100% response rate) providing data for a total of 2144 patients (pre-implementation: n= 1062; post-implementation: n=1082). Significantly increased proportions of patients received care according to the fever (pre: 69%; post: 78%; p=0.0031), hyperglycaemia (pre: 23%; post: 34%; p=0.0085), and swallowing (pre: 42%; post: 51%; p=0.0331) protocols post-implementation. Conclusion: Our results provide rare evidence of successful research translation of Class 1 Level B evidence across an entire state in a short time-frame and in the real world of clinical practice.
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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 | 0.057 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".