High value healthcare analysis of “triggers” in deteriorating patients
Bibliographic record
Abstract
Objective: To review “triggers” for deteriorating patients who required intervention by a medical emergency response team (MET). In addition, to assess whether these “triggers” differed by medical or surgical governance of these patients. A secondary objective was to report laboratory investigations performed via the MET, with particular interest in tests duplicating haemoglobin (Hb) values and their degree of concordance within the context of low-cost, high value inpatient care.Methods: This quality improvement initiative involved a prospective observational cohort of inpatients, who were attended to by the MET at Royal Perth Hospital in Perth, Western Australia over a 2-year period between 2020 and 2022.Results: The mean number of MET calls for inpatients under surgical governance was slightly higher than for those patients under medical governance (1.34 vs. 1.25 calls respectively p = .03). Hypotension triggered a MET call in 184 (40.9%) surgical patients compared to 154 (28%) under medical governance (p < .001). Comparing haemoglobin values obtained from FBP and VBG, Lin’s concordance correlation coefficient (CCC) was found to be 0.986, 95%CI: 0.983, 0.989. The Bland-Altman limits of agreement suggest that the haemoglobin value on a VBG ranges from 9.55 g/L higher than the FBP to 4.7 g/L lower than the FBP.Conclusions: Significant differences in the frequency of triggers for patients under medical vs surgical governance highlight the need for proactive planning around hypotension management of patients under surgical governance. In addition, understanding the nuances between haemoglobin values obtained from FBP and VBG can help with value-based health care and efficiencies in patient care, since measuring haemoglobin values is one of the key components in hypotension management.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".