A Flexible Parental Engaged Consent Model for the Secondary Use of Their Infant’s Physiological Data in the Neonatal Intensive Care Context
Bibliographic record
Abstract
The secondary use of health data, especially the use of physiological data for research holds many opportunities for improving the current understanding of neonatal conditions. As a neonate is unable to provide their consent regarding participation in research studies, a substitute decision maker (SDM) must provide parental or legal guardian consent. However it has been well documented that there are many emotional, mental and physical challenges associated with the parental consent process in the neonatal intensive care unit (NICU). It is proposed that a flexible parental engaged consent model could help alleviate some of these issues by providing parents with the ability to choose and change their clinical engagement level preference for their infant's participation in research at their convenience at any point in time. In this paper, an extension to Service based Multidimensional Temporal Data Mining Framework (STDMn0) to allow for the functionality of flexible patient or surrogate consent is presented based on the use of a flexible consent model initially proposed by Heath [1]. This functionality is demonstrated via an example implementation for a generic retrospective research study in the NICU setting.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".