Prospective monitoring of after‐hours nursing and technologist support calls to a regional Canadian home hemodialysis program
Bibliographic record
Abstract
INTRODUCTION: Increasing renal care providers offer home hemodialysis (HD) as a modality choice. There is considerable variation in the provision of after-hours on-call support for self-dialyzing patients and no literature describing the utility of this service. In this prospective, observational study we sought to monitor and classify the number and nature of interactions between home patients and our on-call nurses and technologists, and enumerate the number of adverse events averted by the availability of on-call staff. METHODS: Our home HD unit provided 24-hour on-call patient support and during a 4-month period in 2012, we prospectively monitored all patient calls to this service. The nature of the calls was logged as nursing-related vs. technical. Call outcomes were classified according to whether patients were able to initiate/resume their treatments or whether additional interventions were required. FINDINGS: During this period, our program cared for 58 home HD patients. Nurses fielded 172 calls and dealt with 239 issues. One hundred nine (46%) were clinical issues including 5 (2%) of a serious nature involving potential harm; 67 (28%) related to machine setup or alarms, 36 (15%) required a technologist to resolve, and 27 (11%) were deemed non-urgent. One hundred six issues were directed to technologists in 99 calls. Issues pertained to machine malfunction (45 calls-43%), machine set-up and alarms (25 calls-24%), or the water system (24 calls-23%). Only 12 calls (11.3%) were not of a technical nature. Nursing and technologist support allowed patients to initiate or continue their treatment 75% and 71% of the time, respectively. DISCUSSION: Home HD on-call services provide patients support to successfully continue their dialysis treatments by troubleshooting clinical and technical aspects of dialysis and by averting 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".