Risk factors for unplanned and crash dialysis starts: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Many patients with kidney failure "crash" onto dialysis or initiate dialysis in an unplanned fashion. There are varying definitions, but essentially, a patient is labeled as having a crash dialysis start if he or she has little to no care by a nephrologist prior to starting dialysis. A patient is labeled as having an unplanned dialysis start when he or she starts dialysis with a catheter or during a hospitalization. Given the high prevalence and poor outcomes associated with crash and unplanned dialysis starts, it is important to establish a better understanding of patient risk factors. METHODS: We will conduct a systematic review and meta-analysis with a focus on both crash and unplanned dialysis starts. The first objective will be to determine patient risk factors for crash and unplanned dialysis starts. Secondary objectives will be to determine the most common criteria used to define both crash and unplanned dialysis starts and to determine outcomes associated with crash and unplanned dialysis starts. We will search MEDLINE, EMBASE and Cochrane Library from inception to the present date for all studies that report the characteristics and outcomes of patients who have crash vs. non-crash dialysis starts or unplanned vs. planned dialysis starts. We will also extract from included studies the criteria used to define crash and unplanned dialysis starts. If there are any eligible randomized controlled trials, quality assessment will be performed using the Cochrane Risk of Bias Assessment Tool. Observational studies will be evaluated using the Newcastle-Ottawa Scale. Data will be pooled in meta-analysis if deemed appropriate. DISCUSSION: The results of this review will inform the design of strategies to help reduce the incidence of crash and unplanned dialysis starts. SYSTEMATIC REVIEW REGISTRATION: Prospero CRD42016032916.
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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 | 0.080 | 0.125 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.043 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".