Stability of Maternal Preferences for Pediatric Health States in the Perinatal Period and 1 Year Later
Bibliographic record
Abstract
BACKGROUND: We have previously shown that parents of premature children provided relatively high valuation of their children's health state in adolescence. However, stability of parental preferences for future pediatric health states is unknown during the antenatal and neonatal periods and infancy. OBJECTIVE: To determine whether preference-based, health-related quality-of-life scores obtained serially from 2 cohorts (women with high-risk pregnancies [antenatal cohort] and mothers of very low-birth-weight newborns [VLBW cohort]) are stable during the first year after birth. DESIGN AND METHODS: Longitudinal cohort study. Participants included 80 high-risk pregnant women recruited at 24 +/- 2 weeks of gestation, and 75 mothers of VLBW infants recruited within 1 week of delivery. We conducted 2 to 3 standardized interviews (antenatally, at 1 week after delivery, and at the 12-month corrected age visit) using the Standard Gamble technique to elicit preferences for 5 pediatric hypothetical health states with varying disabilities. RESULTS: Seventy-three mothers with high-risk pregnancies (91%) and 72 mothers of VLBW infants (96%) completed all scheduled interviews. As expected, preference scores were affected by the level of severity of the hypothetical health states (antenatal cohort, F(4,288) = 87.0 [P<.001]; VLBW cohort, F(4,284) = 64.2 [P<.001]). At each assessment, at least 38% of mothers rated 1 or more health states as worse than death. Repeated-measures analysis showed no change in preference scores over time (antenatal cohort, F(2,144) = 1.3 [P =.29]; VLBW cohort, F(1,71) = 0.7 [P =.42]). Maternal socioemotional factors, infant severity of illness at birth, and global health at 12 months did not affect preference scores. CONCLUSION: In our population, maternal preference scores for disabling health states appear to be stable during the first year of life and are unaffected by key maternal and infant variables.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".