Santa Barbara Ambulance Response for 2006: Performance under load
Bibliographic record
Abstract
Santa Barbara Ambulance Response for 2006: Performance under load Joshua Chang Abstract Hello Introduction Much research has been done on the effect of myocardial infarction survival due to ambulance response time. According to the American Heart Associ- ation, early access to advanced care is a crucial link in the Cardiac Chain of Survival.[3] A study in Ontario, Canada concluded that in order to im- prove survival rates after cardiac arrest, ambulance response times must be reduced and the frequency of bystander-initiated CPR increased[7]. A study performed in King County, Washington determined survival rate to decrease by 2.1% per minute without intervention by Advanced Cardiac Life Support (ACLS). Urban response time in a South-Western metropolitan county of population 620,000 was correlated with myocardial infarction survival rate, and it was found that a response time of under 5 minutes would have a beneficial impact on survival[2]. Similarly, there have been studies done on survival rates for trauma emergencies. A study on survival rates for abdominal gunshot wounds found response time and transport time to be correlated with survival rate.[5]. Another study found that the overall total EMS prehospital time interval was significantly lower for trauma survivors than non-survivors[4]. Materials and Methods Santa Barbara County ambulance dispatch data for the year 2006 was pro- vided by Santa Barbara’s EMS agency for the UCLA Statistics Department’s EMS study group. The data was exported from Santa Barbara’s computer aided dispatch (CAD) system into a Microsoft Access database file that was then exported into an Excel spreadsheet. The Excel spreadsheet was then
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".