Evaluation of the door-to-needle time for fibrinolytic administrationfor acute myocardial infarction
Bibliographic record
Abstract
Background: Fibrinolytic therapy has reduced mortality following acute myocardial infarction (AMI) with the major effect coming from early achievement of infarct-related artery patency. Aim: To evaluate the door-to-needle time for fibrinolytic administration for AMI and to identify factors associated with a prolonged door-to-needle time. Materials and Methods: Our study was a prospective audit of patients who were thrombolyzed for AMI at our hospital from July 1, 2004 to March 15, 2005. All patients admitted with AMI, who were candidates for fibrinolysis, were included. We recorded the door-to-needle time. Whenever possible, we tried to find out the reason for prolonged door-to-needle time. Results: A door-to-needle time of < 30 min could be achieved in 19 of our 35 patients (54.28%). Mean door-to-needle time was 45.25 min. Discussion: Although most guidelines recommend a door-to-needle time of less than 30 min, most hospitals fail to achieve this in most patients. A study conducted by Zed et al. at the Vancouver General Hospital showed that a door-to-needle time of less than 30 min was achieved in only 24.3%. The door-to-needle time achieved at our center was shorter. In most of our patients who were thrombolyzed late, a delay in taking or interpreting an electrocardiogram was responsible. Transfer to the intensive care unit for thrombolysis also resulted in considerable delay. Conclusions: A door-to-needle time of less than 30 mins could be achieved in 19 of our 35 patients (54.28%). A significant number of AMI patients thrombolyzed did not meet the guideline for door-to-needle time of less than 30 min.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".