Assessment of the informed consent related to healthcare decisions for medically fragile child in italy: a pilot study.
Bibliographic record
Abstract
Background: In Italy, the law n. 219/2017 regarding informed consent states that "Communication time between doctor and patient constitutes treatment time". Legal guardian is designated as a proxy to consent on the child's behalf. The issue of proxy informed consent should be approached with a model for parent-child decision-making that is participatory, collaborative, respects and supports the autonomy of child by recognizing their evolving capacities. We aim to assess the informed consent related to healthcare decisions for medically fragile child, using the MacArthur Competence Assessment Tool for Treatment (MacCAT-T). Materials and Methods: An observational study has been conducted at a Child Neuropsychiatry Service, administering a semi-structured interview with customized questionnaire to examine their capacities in four areas of the MacCAT-T. Results were evaluated with the Pearson correlation coefficient for the cognitive and adaptive levels of the Wechsler-Intelligence-Scale-for-Children (WISC-IV) and the Vineland-Adaptive-Behavior-Scales-II (VABS-II). Conclusions: The MacCAT-T domains Understanding, Appreciation, Reasoning, Expressing a Choice were correlated with the cognitive and adaptive levels of the WISC-IV and the VABS-II. Understanding, Appreciation and Expressing a choice have positive correlation with the Communication and Socialization scores of VABS-II; Reasoning has positive correlation with the Working-Memory-Index scores of the WISC-IV. The study enabled to assess the informed consent processes in vulnerable children and although demonstrating how they participate in their care process in a mostly unconscious way, making the frail children more involved in their own care process was possible. Future studies should assess the impact of incorporating MacCAT-T into standard informed consent in other settings.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".