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Record W2164979582 · doi:10.2215/cjn.04021206

Selected Primary Care Issues and Comorbidities in Children Who Are on Maintenance Dialysis

2007· review· en· W2164979582 on OpenAlexaff
Colin T. White, Peter Trnka, Douglas G. Matsell

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2007
Typereview
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of British ColumbiaBC Children's Hospital
Fundersnot available
KeywordsMedicineDialysisPsychosocialNephrologyIntensive care medicineTeamworkPrimary careFamily medicinePediatricsInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Ten-year survival of all children who initiate dialysis at any age now approaches 70%, and in the older child this number is closer to 80%. These children will live with chronic kidney disease and its myriad of associated comorbidities during and throughout their childhood. Their care is complex and requires both teamwork and careful attention paid to maintaining lines of communication among patient, family, and both the facility-based nephrology team and caregivers who are outside the hospital setting. Irrespective of their need for dialysis, children with ESRD deserve and require developmentally appropriate care and anticipatory guidance with respect to primary care issues of childhood. The child who is on dialysis often is cared for solely or in large part by a nephrology service, therefore this review discusses issues that are particularly important to pediatric nephrologists in relation to selected primary care issues and comorbidities for the child who is on dialysis, with an emphasis on medical and psychosocial issues, and with particular weight placed on issues that are pertinent to the adolescent dialysis patient.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.131
GPT teacher head0.499
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2007
Admission routes1
Has abstractyes

Explore more

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