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Record W2100212071 · doi:10.3747/pdi.2009.00265

Attitudes of Caregivers to Management of End-Stage Renal Disease in Infants

2011· article· en· W2100212071 on OpenAlexaffabout
Jun Chuan Teh, Michelle L. Frieling, Julianna Sienna, Denis F. Geary

Bibliographic record

VenuePeritoneal Dialysis International · 2011
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPeritoneal dialysisMedicineEnd stage renal diseaseIntensive care medicineKidney diseaseStage (stratigraphy)DiseaseInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.042
GPT teacher head0.351
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations58
Published2011
Admission routes2
Has abstractyes

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