A detailed analysis of patients included in the Summary Hospital-level Mortality Indicator (SHMI) for myocardial infarction (MI)—all is not what it seems?
Bibliographic record
Abstract
BACKGROUND: The Summary Hospital-level Mortality Indicator (SHMI) for Myocardial Infarction (MI) is the ratio of the observed to the expected number of deaths due to MI. We aimed to assess (1) the accuracy of MI as a diagnosis in the SHMI for MI and (2) the healthcare received by patients with type 1 MI included in the SHMI for MI. METHODS: Retrospective review of patients included in SHMI for MI from April 2017 to March 2018. The diagnosis of MI was divided into type 1, type 2 and non-MI. For patients with type 1 MI who underwent intervention, we applied the prognostic Toronto Risk Score (TRS) and classified into group 0: score <13 (mortality risk 0%-4%, lowest risk), group 1: score 13-16 (mortality risk 6%-19.6%), group 2: score 17-19 (mortality risk 27.4%-47.6%) and group 3: score ≥20 (mortality risk 58%-92%). For patients with type 1 MI who underwent conservative management, we reviewed appropriateness of conservative management. RESULTS: SHMI for MI was 96 (41/42.83) falling to 65.4 with the inclusion of only type 1 MI (28 patients, 28/42.83). About 41.5% (n=17) underwent intervention of whom three were in the lowest risk TRS (group 0) and all received appropriate healthcare. Conservative management was appropriate for the 26.8% (n=11) treated medically, the most common reason was severe cognitive dysfunction. CONCLUSIONS: We have demonstrated that SHMI for MI can be inaccurate due to the inclusion of type 2 MI or non-MI. Grouping patients into intervention versus conservative management helps in assessment of healthcare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".