The use of combined physiological parameters in the early recognition of the deteriorating acute medical patient
Bibliographic record
Abstract
BACKGROUND: Early warning scores (EWS) are widely used to allow early recognition of the deteriorating patient. We aimed to test their ability to predict major deterioration in medical patients. METHODS: Two cohorts were prospectively identified who were admitted to an acute medical admissions unit and to the respiratory unit but not admitted to the intensive care unit (ICU): medical-non ICU and respiratory-non ICU groups. Two further cohorts were retrospectively identified that required ICU admission from these units (medical-ICU and respiratory-ICU groups). Discriminant analysis and receiver operating characteristic curves were used to discriminate between groups, and time relationships were analysed. RESULTS: Heart rate (HR) and respiratory rate (RR) were significantly higher--and oxygen saturation (SaO₂) significantly lower--in the medical-ICU group as compared with the medical non-ICU group, and in the respiratory-ICU group as compared with [corrected] the respiratory-non ICU group. Discriminant functions incorporating HR, RR and SaO₂ performed at least as well as existing EWS systems in predicting ICU admission. CONCLUSIONS: Commonly used physiological parameters and existing EWS systems are useful at identifying sick patients. The discriminant functions described here appear to have a role in this setting but require validation in future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".