Intelligent “Internet Plus” services in the first case of home hemodialysis in mainland China
Bibliographic record
Abstract
BACKGROUND: Many studies have shown that compared with those who use other dialysis modalities, patients using home hemodialysis (HHD) have an increased rate of survival and better quality of life. It was noted in 2006 that there was opportunity for significant expansion of the use of HHD in many countries. China covers a vast area and has a large amount of end-stage renal failure patients. But in mainland China, all dialysis treatments are in-center, and the number of HHD patients is zero. In 2018, our hospital received the permission of the Shanghai government to carry out HHD. CASE PRESENTATION: We initiated four incident hemodialysis patients on an HHD regimen, one patient has been dialyzed in the home safely for 8 months. The biochemical parameters of the first patient remained stable on the regimen and he achieved standard Kt/V urea targets. Treatment-related adverse events were not reported during the follow-up. We combined HHD with intelligent "Internet Plus" real-time remote monitoring and introduced the Internet, especially visualization software, to replace traditional telephone and home visit methods. It is more intuitive and quicker to assist patients in performing home hemodialysis and improve the safety of treatment. CONCLUSIONS: HHD can be performed by selected trained patients in mainland China. Combined with the internet, visualization software, and traditional telephone and home visits, it is intuitive and quick to assist patients in carrying out HHD and improve the safety of treatment. HHD broadens the choices for uremia patients in China.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".