The Effect of In-Hospital Intervention to Reduce Door to Needle Time in Patients Receiving Tissue Plasminogen Activator
Bibliographic record
Abstract
Background and Objective: Attempts have been made to confirm the diagnosis of stroke at the earliest stage and to prevent the development of neurological deficits. Tissue plasminogen activator (tPA) plays a logical role in the treatment of acute stroke by converting plasminogen to plasmin, and recent studies have shown that the drug can be injected up to four and a half hours after the onset of symptoms. The present study aimed to evaluate the effect of an in-hospital intervention to reduce door to needle (DTN) time in acute stroke patients. Methods: This epidemiological case-control study was performed on patients with acute ischemic stroke from September to March 2016 (n= 25) (group A) who were treated with tPA according to stroke guidelines. Their basic specifications, DTN and Door to Computed Tomography scan (DTC) time were recorded. Then, from April to August 2017, an intra-hospital recipe for tPA injection was provided by investigating the obstacles and causes of intra-hospital delays. Subsequently, stroke patients receiving tPA from September to March 2017 (n= 23) (group B) were examined, and their DTN and DTC were compared with patients in the first group. Results: The mean DTN and DTC in group A were 67.27±28.83 and 30.40±10.59 minutes, and in group B, were 45±25.98 and 22.17±8.50, minutes, respectively, which made a significant difference between the two groups (P=0.005, P=0.006, respectively). The percentage of patients with DTN less than 60 minutes increased from 52% in group A to 95.6% in group B. The percentage of patients with DTC less than 25 minutes decreased from 32% to 69.56% (P<0.001). The percentage of patients with symptomatic cerebral haemorrhage increased from 12% to 8.7% (P<0.001). The percentage of patients with independent ambulatory (mRS: 0-2) at three months after discharge increased from 48% to 56.5% (P=0.003). The mortality rate also decreased from 24% to 13.4% (P<0.001). Conclusion: By resolving the causes of intra-hospital delays and using a proper team program, the mean DTN and DTC of patients receiving tPA were reduced. This decrease in DTN time was accompanied by reduced complications in the form of reduced symptomatic cerebral haemorrhage and mortality and improved prognosis.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".