Bibliographic record
Abstract
Finland is geographically a rather large country with a relatively sparse population (5.3 million). Home hemodialysis (HHD) was started in Helsinki 40 years ago and in the early years it was only used in selected patients. However, by the late 1980s HHD almost disappeared owing to the advent of CAPD and new HD centers. Towards the end of the 1990s, it became evident that PD had limitations and new ways had to be found to individualize HD, improve the outcome, increase capacity, and limit the growth of costs of HD. After careful planning, HHD was reinstituted at the Helsinki University Hospital in 1998 and since then the program has grown steadily. By December 31, 2007, altogether 163 patients had started at home. This has required changes in the predialysis program where the "home first" policy was adopted. Other important features include close cooperation with other nephrological centers as well as centralized HHD training that also supports more remote hospitals. Since then this therapy has been started in several other academic and in some smaller hospitals, and at the end of last year about 4% of all Finnish dialysis patients (n=1.600) were on HHD (prevalence 11.8/million). In the Helsinki metropolitan area this treatment is the most economical modality (estimated annual global costs euro37.000), comparable to self-care satellite HD and CAPD. A successful HHD program requires a well-organized predialysis program, a highly motivated multidisciplinary team, and well-developed training networks.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".