Abstract 18406: Are Medical Emergency Team Call Event Rates to the Radiology Department Greater Than to the General Hospital Wards?
Bibliographic record
Abstract
Objective : To establish if hospital in-patients are at greater risk for requiring Medical Emergency Team (MET) assistance while in the Radiology Department, we determined the event rates of MET calls to the Radiology department (MET-RD) and the general hospital wards (MET-W). Design/Participants : Retrospective review of in-patients experiencing a MET call in 2009. Setting : Tertiary care hospital with a well-established MET system admitting 32,000 patients and performing 160,000 in-patient Radiology (RD) procedures per year. Measurements : The number of MET-RD was adjusted for length of stay (LOS) to determine the number of MET-RD/hour/1000 RD admissions. The number of MET-W was adjusted for LOS to determine the number of MET-W/hour/1000 hospital admissions. Main Results : The MET-RD event rate was slightly higher than MET-W (0.42 v. 0.31 events/hour/1000 hospital admissions) but not statistically different (p=0.73). The overall average LOS in the RD (LOS-RD) was 58 min. General x-ray (XR) comprised 63% of RD admissions but only 11% of MET-RD (LOS 40 min, XR event rate 0.09). Event rates and LOS in the radiology specialty modality areas revealed that 38% of MET-RDs occurred in computed tomography (CT) (average CT LOS 47 min) and an event rate (0.94) over twice the RD average. Magnetic resonance imaging (MRI) represented only 5% of the RD admissions but had 27% of MET-RD (average MRI LOS-RD 90 minutes) and an event rate (1.43) that was 3.5 times higher than the RD average. The longest LOS-RD was in Nuclear medicine (NM; 111 minutes) and represented only 1% of RD admissions but 5% of MET-RD, with an NM event rate (1.34) that was 3.2 times higher than the RD average. Analysis of event rates for the combined RD specialty modalities (minus XR) revealed an average RD specialty modality event rate that was significantly higher when compared to MET-W (0.76 v. 0.31, p=.007). Conclusions : Although overall MET-RD event rates were similar to MET-W, they were unevenly distributed across patients receiving CT, MRI and NM studies. The combined event rate in the RD specialty modalities suggest increased MET risk for patients in these areas, which may have implications for RD surveillance practices.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".