AB039. Partnering with children with medical complexity and their families to improve health outcomes and reduce cost
Bibliographic record
Abstract
In the US and Canada, children with medical complexity (CMC) comprise 1–3% of all children, but account for up to a third of child health expenditures. Care for this vulnerable population is a significant cost to the healthcare system and individual families. However, most research has been done to manage these patients after they become complex rather than identifying children at risk and assisting with informed decision making early in the child’s healthcare journey. Often parents are often bombarded with multiple lifesaving decisions in the course of their child’s illness with minimal preparation, education, or long term planning; only to be “left” at the end when no cure or correction is found. Continued medical advances will only add to these numbers. Healthcare must move from a short term, reactive approach to a proactive partnership for the care of CSHCN. The keys to successful partnership include early identification of children with complexity, understanding of the parent/child goals of care, discussion of both short and long term outcomes of medical decisions, and flexibility of the medical team to modify care options to meet the needs of the family.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.332 | 0.043 |
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".