Integrating parent voices into research at the extremes of prematurity: what are we doing and where should we go?
Bibliographic record
Abstract
When a baby is born premature, a landscape of potential problems replaces an imagined future. Outcomes become the measures of success. Researchers are recognizing that we need the direct input of parents to select meaningful outcomes. In this article, we describe how researchers and clinicians in neonatology have historically defined outcomes and the limitations of these methods. We chart the integration of stakeholders-patients and parents-into outcomes selection. 'Parent-important outcomes' are those deemed most important by parents, as the voices of their children. We outline a path toward determining parent-important outcomes in neonatology through mixed methods research. We conclude by suggesting how parent-important outcomes can be integrated into neonatal follow up research and clinical trial design. Ultimately, all researchers of prematurity aim in some way to improve outcomes that parents and patients care about. We hope this article will remind us of this beacon.
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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 | 0.073 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".