Quality Assurance Audit of Technique Failure and 90-Day Mortality after Program Discharge in a Canadian Home Hemodialysis Program
Bibliographic record
Abstract
Background and objectives Little is known about patients exiting home hemodialysis. We sought to characterize the reasons, clinical characteristics, and pre-exit health care team interactions of patients on home hemodialysis who died or underwent modality conversion (negative disposition) compared with prevalent patients and those who were transplanted (positive disposition). Design, setting, participants, & measurements We conducted an audit of all consecutive patients incident to home hemodialysis from January of 2010 to December of 2014 as part of ongoing quality assurance. Records were reviewed for the 6 months before exit, and vital statistics were assessed up to 90 days postexit. Results Ninety-four patients completed training; 25 (27%) received a transplant, 11 (12%) died, and 23 (25%) were transferred to in-center hemodialysis. Compared with the positive disposition group, patients in the negative disposition group had a longer mean dialysis vintage (3.15 [SD=4.98] versus 1.06 [SD=1.16] years; P =0.003) and were performing conventional versus a more intensive hemodialysis prescription (23 of 34 versus 23 of 60; P <0.01). In the 6 months before exit, the negative disposition group had significantly more in-center respite dialysis sessions, had more and longer hospitalizations, and required more on-call care team support in terms of phone calls and drop-in visits (each P <0.05). The most common reason for modality conversion was medical instability in 15 of 23 (65%) followed by caregiver or care partner burnout in three of 23 (13%) each. The 90-day mortality among patients undergoing modality conversion was 26%. Conclusions Over a 6-year period, approximately one third of patients exited the program due to death or modality conversion. Patients who die or transfer to another modality have significantly higher health care resource utilization ( e.g. , hospitalization, respite treatments, nursing time, etc. ).
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".