Anticipating pediatric patient transfers from intermediate to intensive care
Bibliographic record
Abstract
Abstract BACKGROUND Paediatric intermediate care units (IC) function to provide a higher level of inpatient paediatric care such as frequent monitoring or nursing intervention compared to routine inpatient general paediatric care. A small subset of these patients in IC deteriorate further and require transfer to the paediatric intensive care unit (PICU). By identifying patient characteristics at the time of admission that predict secondary transfer, specific monitoring, resource allocation and early intervention may be implemented in order to improve quality of care. Appropriate and timely patient flow and length of stay (LOS) can also be optimized. DESIGN/METHODS The IC at our tertiary care institution admits predominantly general paediatric patients. Its admission criteria have been designed with input from stakeholders, and comprise a range of physiologic and resource based measures. Data were collected on patients who were admitted to IC, including those subsequently transferred to PICU, between July 2016 - June 2017. Patients whose index IC admission was from the PICU were excluded. Data included demographic and physiologic characteristics (heart rate, respiratory rate, temperature, oxygen therapy) and the bedside paediatric early warning system (BPEWS) score, a validated score based on vital signs. Quantitative and qualitative data were analyzed using Fisher and Mann-Whitney tests respectively. RESULTS 210 patient visits occurred in this time period, with 44 (20.95%) transferred to PICU (Table 1). Transferred patients showed no significant difference in age or sex. However, they had significantly higher median BPEWS, heart rate, respiratory rate and mean body temperature compared to non-transferred patients, as well as a significantly higher rate of respiratory support and shorter LOS on IC. There was a non-significant trend toward admission directly from the Emergency Department (ED) in transferred patients. Admission criteria and main organ systems affected were similar amongst both groups, with a predominance of respiratory conditions. PICU transfer was predicted by most physiological characteristics, including BPEWS. This coupled with a significantly shorter length of stay is a likely reflection of higher disease acuity in this group of patients and higher risk of deterioration and subsequent transfer to PICU. CONCLUSION The need for close monitoring of physiologic parameters remains paramount in predicting the need for transfer from the IC to PICU.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".