State of the art in clinical decision support applications in pediatric perioperative medicine
Bibliographic record
Abstract
PURPOSE OF REVIEW: The goal of this review is to describe the recent improvements in clinical decision tools applied to the increasingly large and complex datasets in the pediatric ambulatory and inpatient setting. RECENT FINDINGS: Clinical decision support has evolved beyond simple static alerts to complex dynamic alerts for: diagnosis, medical decision-making, monitoring of physiological, laboratory, and pharmacologic inputs, and adherence to institutional and national guidelines for both the patient and the healthcare team. Artificial intelligence and machine learning have enabled advances in predicting outcomes, such as sepsis and early deterioration, and assisting in procedural technique. SUMMARY: With more than a decade of electronic medical data generation, clinical decision support tools have begun to evolve into more sophisticated and complex algorithms capable of transforming large datasets into succinct, timely, and pertinent summaries for treatment and management of pediatric patients. Future developments will need to leverage patient-generated health data, integrated device data, and provider-entered data to complete the continuum of patient care and will likely demonstrate improvements in patient outcomes.
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 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) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".