On-line Monitoring of Nocturnal Home Hemodialysis
Bibliographic record
Abstract
Background. Nocturnal home hemodialysis (NHD, 6–7 times weekly 6–8 hours) is a promising dialysis modality. On-line distant monitoring is complicated and expensive, and its usefulness should be evaluated. Methods. Since December 2001, 15 patients were included in a Dutch NHD project (‘Nocturne’). So far, 3 patients received a renal transplant. Patients are assisted by their spouses. The dialysis machine is connected through the public telephone network by a bedside node and routers to the server in a call center. All patients received a dedicated ISDN-connection. Alarms produced by the machine are detected in the call center. For each type of alarm, a period is defined during which the patient can solve the problem. When the alarm continues after this period, the call center will notify the patient. Results. During 4 months, approximately 900 alarms in 1300 dialysis treatments were produced. In only 11 of 900 cases, the partner had to wake up the patient because he/she did not hear the alarm. The call center had to call 13 times, always because the patient resumed sleeping after the end of the treatment. No intervention because of serious problems was required. A majority of patients and personnel consider on-line monitoring nevertheless important as it gives a sense of safety. Additionally, nurses use the real-time connection frequently to check running dialysis treatments. Also, the system enables automatic saving of important treatment data in an electronic patient file. The experience so far is used to design a so-called ‘secure bitpipe’ for homecare applications, with emphasis on privacy, safety, security and effectivity. Conclusion. On-line monitoring of NHD may not be crucial, but enables good coaching of patients and gives a sense of safety.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".