Lapses in safety in end‐stage renal disease patients admitted to surgical services
Bibliographic record
Abstract
Chronic dialysis patients are a vulnerable population that may be highly susceptible to medical errors, particularly when they are hospitalized. We performed a chart review of chronic hemodialysis patients admitted to surgical services at a tertiary care center in order to characterize lapses in patient safety. We conducted a retrospective chart review of admissions of patients receiving chronic hemodialysis to various surgical services at St. Michael's Hospital from January 1, 2009 to December 31, 2010. For each hospitalization, we collected data on four process of care indicators of potential safety lapses. When these lapses were identified, we sought to determine whether: (i) the lapse was detected and remedied; and (ii) the lapse resulted in an adverse event. Among the 41 patients, 96 process of care lapses were identified. Multiple lapses were detected in 83% of the cohort. Failure to order a renal diet (72%) occurred most often. There was one adverse event. Process of care lapses were detected 39% of the time, usually within 1.5 days of their occurrence. Patients receiving chronic hemodialysis admitted to surgical services experience multiple lapses in patient safety, which often remain undetected. As such, it is imperative that these patients be closely monitored in order to mitigate against potential adverse events.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".