Abstract P-412: UTILIZATION OF ACUITY-BASED MONITORING BUNDLES
Bibliographic record
Abstract
Aims & Objectives: Data driven decision making relies on having appropriate data in a sufficient quantity and quality that is consistently and reliably available. Standardized approaches to patient monitoring could potentially reduce variability in practice and ensure that monitoring strategy consistently matches patient acuity. Methods In the Cardiac Critical Care Unit (CCCU) at the Hospital for Sick Children in Toronto, we have an existing traffic-light system (red, yellow, green) for ordering laboratory investigations after surgery that is based on anticipated patient acuity and cardiac procedure. A similar approach was used to develop acuity-based patient monitoring bundles for physiologic variables. Results Diagnosis and procedure driven indications for monitoring bundles were developed by staff consensus (Table 1). Monitoring for each of the red, yellow, and green categories is influenced by the monitoring strategy established in the operating room, and reduced in intensity and invasiveness with decreased acuity. Changes to monitoring can be made at anytime by staff based on patient course and stability. Conclusions Acuity-based patient monitoring helps standardize relevant physiologic variables to be monitored and prevent harm associated with excessive and insufficient patient assessment. Consistent monitoring approaches can ensure availability of appropriate data for event review and research. When monitoring bundles are consistently implemented, they may optimize resource allocation, provide surrogate measures of patient acuity and course and improve awareness of patient vulnerability. Next steps include tracking the frequency of unexpected patient transition between categories and effects of implementation using outcome, process and balancing measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".