Factors influencing dialysis withdrawal: a scoping review
Bibliographic record
Abstract
BACKGROUND: Research on factors associated with dialysis withdrawal is scarce. This study examined the predictors that might influence rate of dialysis withdrawal. Existing literature is summarized, analyzed and synthesized to identify gaps in the literature with regard to the factors associated with dialysis withdrawal. METHODS: This scoping review used a systematic search to synthesize research findings related to dialysis withdrawal and identified gaps in the literature. The search strategy was developed and applied using PubMed, EMBASE and CINHAL databases. The selection criteria included articles written in English and published between 1997 and 2016 that examined dialysis withdrawal and associated factors in patients with any modality of renal dialysis.. Case reports and studies only including renal transplant patients were excluded. Fifteen articles were selected in accordance with these selection criteria. RESULTS: The literature review revealed a scarcity of research on dialysis withdrawal and associated factors. Furthermore, the study findings were inconsistent and inconclusive. Authors have defined dialysis withdrawal in terms of dialysis discontinuation, withholding, death, withdrawal, treatment refusal/cessation, or technique failure. Authors have selected homogeneous patient population on either hemodialysis (HD) or peritoneal dialysis (PD) patients, thus making comparisons of studies and generalization of findings difficult. CONCLUSION: Future studies should explore the influence of both HD and PD on patient-elected dialysis withdrawal using a large a priori calculated sample size.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".