Evidence, Quality, and Waste: Solving the Value Equation in Neonatology
Bibliographic record
Abstract
Rising health care costs challenge governments, payers, and providers in delivering health care services. Tremendous pressures result to deliver better quality care while simultaneously reducing costs. This has led to a wholesale re-examination of current practice methods, including explicit consideration of efficiency and waste. Traditionally, reductions in the costs of care have been considered as independent, and sometimes even antithetical, to the practice of high-quality, intensive medicine. However, it is evident that provision of evidence-based, locally relevant care can result in improved outcomes, lower resource utilization, and opportunities to reallocate resources. This is particularly relevant to the practice of neonatology. In the United States, 12% of the annual birth cohort is affected by preterm birth, and 3% is affected by congenital anomalies. Both of these conditions are associated with costly health care during, and often long after, the NICU admission. We will discuss how 3 drivers of clinical practice in neonatal care (evidence-based medicine, evidence-based economics, and quality improvement) can together optimize clinical and fiscal 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".