Attitudes of Caregivers to Management of End-Stage Renal Disease in Infants
Bibliographic record
Abstract
OBJECTIVES: To characterize the attitudes of pediatric nephrologists caring for infants with end-stage renal disease (ESRD) compared with attitudes from a survey published in 1998. Nephrology nurses and social workers were included. METHODS: An e-mail survey was distributed to pediatric nephrology teams in Canada, Germany, Japan, the United Kingdom, and the United States. RESULTS: Survey responders totaled 270. Renal replacement therapy (RRT) is offered by all nephrologists to some children 1-12 months, and by 98% to some less than 1 month of age (93% in 1998). Of responding nephrologists, 30% offer RRT to all children less than 1 month of age (41% in 1998), and 50%, to all children 1-12 months. Among respondents, 50% indicated that parents can never refuse RRT for children aged 1-12 months, compared with 27% for younger infants. The most influential factor in rejecting RRT for infants was the presence of a co-existing abnormality. Nurses were more likely to believe that parents have the right to refuse RRT for infants. CONCLUSIONS: Attitudes of pediatric nephrologists have changed since 1998. Also, nurses have opinions that are different from those of the nephrologists on some issues, and a consensus should be reached before speaking to families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| 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 | 0.000 |
| 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".