Declining trend of peritoneal dialysis: a single-center experience.
Bibliographic record
Abstract
Peritoneal dialysis (PD), despite being advantageous to patient, physician, and society, has failed to show the growth it deserves. On the contrary, PD utilization has declined. Over the past several years, we have noticed a decline in the number of our home dialysis patients. When compared to the national trend, we find our trend to be not significantly different from other centers across the country. A similar trend has also been noticed in Canada. Although several reasons may exist for the decline, we intend to concentrate on local factors. In the first quarter of 1996, we had a total of 46 adult and pediatric end-stage renal disease (ESRD) patients on PD. That number decreased to 23 at the end of fourth quarter of the year 2001. The losses in our program far exceeded the gains. We lost our patients mainly to in-center hemodialysis (ICHD) and to transplantation. Peritonitis and membrane failure remained the major grounds for the loss to ICHD. In our center, geographic location and a lack of structured pre-ESRD education probably played a major role in the decline. Many of our patients are from distant counties that have a contract with University of Texas Medical Branch for providing health care to their indigent population. However, once those patients develop complications, the counties rely on the expertise of local physicians and nephrologists.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".