Development of a proxy-reported pulmonary outcome scale for preterm infants with bronchopulmonary dysplasia
Bibliographic record
Abstract
BACKGROUND: To develop an accurate, proxy-reported bedside measurement tool for assessment of the severity of bronchopulmonary dysplasia (also called chronic lung disease) in preterm infants to supplement providers' current biometric measurements of the disease. METHODS: We adapted Patient-Reported Outcomes Measurement Information System (PROMIS) methodology to develop the Proxy-Reported Pulmonary Outcomes Scale (PRPOS). A multidisciplinary group of registered nurses, nurse practitioners, neonatologists, developmental specialists, and feeding specialists at five academic medical centers participated in the PRPOS development, which included five phases: (1) identification of domains, items, and responses; (2) item classification and selection using a modified Delphi process; (3) focus group exploration of items and response options; (4) cognitive interviews on a preliminary scale; and (5) final revision before field testing. RESULTS: Each phase of the process helped us to identify, classify, review, and revise possible domains, questions, and response options. The final items for field testing include 26 questions or observations that a nurse assesses before, during, and after routine care time and feeding. CONCLUSIONS: We successfully created a prototype scale using modified PROMIS methodology. This process can serve as a model for the development of proxy-reported outcomes scales in other pediatric populations.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".