The CLOQS Trial Protocol: A Cluster-Randomized Trial Evaluating a Simple, Low-Cost Intervention to Reduce Treatment Times in Acute Stroke
Bibliographic record
Abstract
RATIONALE: In acute stroke, time is brain: faster tissue plasminogen activator treatment improves patient outcomes. Published guidelines for door-to-scanner time are <25 minutes, and for door-to-needle time <60 minutes. These benchmarks are rarely met. Paradoxically, the earlier a stroke patient arrives to hospital, the longer treatment takes. There is an urgent need to shift focus away from the 4.5 hour time window, towards treatment times <60 minutes. AIMS: The objective of the Countdown Lights to Optimize Quality in acute Stroke (CLOQS) trial is to determine whether a simple, low-cost organizational behavior intervention, a large, red stopwatch timer attached to the stretcher upon arrival, will decrease door-to-scanner and door-to-needle treatment times for tissue plasminogen activator-treated patients. DESIGN: A multicenter, time-clustered randomized control trial. The stopwatch timers will be used in Emergency Departments for all acute stroke patients across the University of Toronto Stroke Program. The order of intervention (ON) and control (OFF) blocks will be randomly assigned in a 1:1 ratio over an 18 month period. Blocks will be weighted in a 2:1 ratio of ON/OFF using a permuted block design (ON blocks last two weeks; OFF blocks last one week). STUDY OUTCOMES: The primary end-point is percentage of patients achieving best-practice guidelines (door-to-needle treatment time <60 minutes). Secondary end-points are median time intervals for 1) door-to-scanner and 2) door-to-needle times during ON versus OFF blocks. Tertiary end-points are in-hospital mortality and time series analysis to determine change in treatment times from prior to study onset through study completion.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".