26 Not What Clinicians Thought: Decisional Regret in Parents of Extremely Preterm Children
Bibliographic record
Abstract
Abstract Background Preterm birth is associated with higher risk of death and severe neurodevelopmental impairment. There is an increased risk in extremely preterm infants, raising questions among ethicists and clinicians as to whether providing active care to infants born at the lower extreme is worth the outcomes, and if these outcomes are a source of decisional regrets for parents. Objectives Explore decisional regrets in parents of extremely preterm children. Design/Methods We consecutively recruited all parents of infants born <29 weeks’ gestational age, aged between 18 months corrected age and 7 years, and seen for neonatal follow-up at a single tertiary center over a one-year period. We asked the following question: “Knowing what you know now, is there anything you would have done differently?” Answers were analyzed independently by two reviewers using qualitative methodology, and discrepancies were resolved by a third reviewer. Mixed methods were used to examine the frequency of each theme and associate parental answers to demographic and clinical factors. Results Responses were obtained from 249 parents (98% participation rate). The following main themes emerged: (1) Nothing – I did what I could or was told to do: 53%; (2) Regrets about self-care: 31%; “I would listen to the nurses’ advice to sleep more” (3) Guilt related to the impression preterm labor could have been prevented by them or the medical team: 19%; “I would have pushed for better care and monitoring during pregnancy. I felt as though I wasn’t listened to when I thought I was in labour when sure enough I was” (4) Regrets about parental role in decision-making: 15%; “I would speak up more at the beginning of the hospitalisation”. None of the parents reported on regretting any life-and-death decisions they made at birth and in the neonatal unit. Conclusion In our cohort, more than half of parents of surviving preterm infants did not have any regrets associated to their NICU experience. However, lessons can be learned to improve parental support, self-care and solutions to improve their role as parents. Unlike what can be stated using “opinion-based medicine”, limiting or forgoing intensive care is not a solution to eliminate decisional regrets in parents.
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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 | 0.016 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".