Prospective cohort study on the incidence and risk factors of emergency home visits among Japanese home care patients
Bibliographic record
Abstract
BACKGROUND: Population aging requires more physician home visits, and various measures need to be taken to reduce the burden on visiting physicians. However, the incidence and associated factors of burdensome emergency home visits remain unclear. We aimed to reveal the incidences of emergency home visits among cancer and noncancer patients and examine how visiting nurses affect those. METHODS: We performed a prospective cohort study across three clinics in Japan and enrolled the patients receiving home visits within a 3-month study period. We calculated the incidence rates using person-time at risk and conducted a Cox regression in the analysis of risks for emergency home visits. RESULTS: A total of 278 patients were analyzed. The incidences of emergency home visits among the overall, the cancer, and the noncancer home care patients were 1.61, 7.23, and 1.37 per 10 person-months, respectively. The adjusted hazard ratios of a cancer-bearing state and visiting nurse service use were 4.71 (95% confidence interval [CI], 2.60-8.52) and 1.85 (95% CI, 1.77-1.94), respectively. CONCLUSIONS: The incidence of emergency home visits among cancer patients was around five times greater than noncancer patients. Our study did not demonstrate that visiting nurses prevent emergency home visits. Further studies are needed to clarify how visiting nurses reduce physicians' burden.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".