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Record W2171129469 · doi:10.3747/pdi.2012.00083

Outcome of Acute Peritoneal Dialysis in Northern Tanzania

2012· article· en· W2171129469 on OpenAlexaff
Kajiru Kilonzo, Sudakshina Ghosh, Siya Anaeli Temu, Venance P. Maro, John Callegari, Mary Carter, Garry J. Handelman, Fredric O. Finkelstein, Nathan W. Levin, Karen Yeates

Bibliographic record

VenuePeritoneal Dialysis International · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicinePeritoneal dialysisIntensive care medicineNephrologyTanzaniaAcute kidney injuryDialysisReferralEmergency medicineInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

Data on the burden of acute kidney injury (AKI) in resource-poor countries such as Tanzania are minimal because of a lack of nephrology services and an inability to recognize and diagnose AKI with any certainty. In the few published studies, high morbidity and mortality are reported. Improved nephrology care and dialysis may lower the mortality from AKI in these settings. Hemodialysis is expensive and technically challenging in resource-limited settings. The technical simplicity of peritoneal dialysis and the potential to reduce costs if consumables can be made locally, present an opportunity to establish cost-effective programs for managing AKI. Here, we document patient outcomes in a pilot peritoneal dialysis program established in 2009 at a referral hospital in Northern Tanzania.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.363
Teacher spread0.333 · 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

Citations83
Published2012
Admission routes1
Has abstractyes

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