Retrospective Assessment of the Standardized Mortality Ratio as a Measure of the Quality of Care in a Major Intensive Care Unit in the Republic of Cyprus
Bibliographic record
Abstract
Introduction: The standardized mortality ratio (SMR) is commonly used to assess the overall quality of care by comparing the observed hospital mortality with the mortality predicted by statistical models. If the observed deaths are less than the predicted, the overall quality of care can be considered high; in the opposite case, it is low. Aim: The aim of the study was to assess the overall quality of care in an intensive care unit (ICU) during the period of 2012 to 2017. We also reported our experience and lessons learned throughout the surveillance period. Methods: A retrospective study design was adopted. Healthcare-associated infections (HAI–ICU) protocol v1.1 was used in a major ICU for a period of 6 years. All patients admitted to the ICU during the surveillance period were included in the study. The SMR was measured. Results: During the 6-year period, 1067 patients were admitted and remained hospitalized for more than 48 hours; 207 patients' discharge status was reported as “death”, compared to 309 deaths predicted based on the SAPS II score. The overall mean observed mortality rate during the study period was 19.4%, as opposed to 28.95% for the predicted mortality. The overall mean SMR was 0.62 (IQR 0.49-0.82). Difficulties were faced due to the lack of surveillance software, but they were overcome by the use of a freely available web-based form. Conclusions: The overall quality of ICU care is considered to correspond to high-quality standards, since standardized mortality rates during the study period were lower than one. The use of the web-based form as an alternative solution to the surveillance software performed well in terms of recording data.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".