Combined Physician-Parent Decision Support tool for the neonatal intensive care unit
Bibliographic record
Abstract
In this paper we explore the design options for the development of a combined physician-parent decision support tool for a neonatal intensive care unit (NICU), known as PPADS or Physician and Parent Decision Support. The work incorporates previous research on the development of a clinical data repository and the development of an NICU clinical decision support system (CDSS) for physicians that predicts mortality and a number of potential complications. In this work, we are combining the physician and parent tools to develop a system designed to facilitate parents' involvement in the shared decision-making process for difficult treatment decisions for their critically ill babies in the NICU. We have described the framework and architectural design for developing the combined physician-parent tool. We identified the design criteria and mode of operation, as well as the standards that apply to this type of decision system. We also explored the existing open-source technologies that would best fit this type of design. Finally, we have developed a prototype of the system that was evaluated by our physician partner and a nurse decision support specialist. Future work will involve a pilot usability study in a tertiary level NICU.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".