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The Practice of Dialysis in the Developing Countries

2003· article· en· W1991110163 on OpenAlexvenueno aff
Vivekanand Jha, Kirpal S. Chugh

Bibliographic record

VenueHemodialysis International · 2003
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDialysisIntensive care medicinePeritoneal dialysisReimbursementContinuous ambulatory peritoneal dialysisDeveloping countryHemodialysisMalnutritionPopulationSurgeryEconomic growthHealth careEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

There are few organized data on the practice of dialysis in developing countries, mostly because of a lack of renal registries. The economic, human, and technical resources required for long-term dialysis make it a major economical and political challenge. Most countries do not have not well-formed policies for treatment of end-stage renal disease. The dialysis facilities are grossly inadequate, and there are no reimbursement schemes to fund long-term dialysis. Hemodialysis units are mostly in the private sector and consist of small numbers of refurbished machines. Water treatment is frequently suboptimal, and this problem has led to a number of complications. Hepatitis B and C infections are widespread in dialysis units. Continuous ambulatory peritoneal dialysis (CAPD) seems to be the ideal dialysis option for patients living in remote areas, but high costs preclude its widespread usage. The Mexican experience suggests that even after it becomes affordable, CAPD needs to be used judiciously. Inadequate dialysis, infections, and malnutrition account for the high mortality among the dialysis population in developing countries. Acute peritoneal dialysis using rigid stylet-based catheters is the main form of dialysis in remote areas. Pediatric dialysis units are almost nonexistent. A significant lack of resources exists in developing countries, making the provision of highly technical and expensive care like dialysis a challenge.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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)0.0000.000

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 head0.015
GPT teacher head0.295
Teacher spread0.281 · 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.

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

Citations38
Published2003
Admission routes1
Has abstractyes

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