Insights into nephrologist training, clinical practice, and dialysis choice
Bibliographic record
Abstract
There is variable emphasis on dialysis-specific training among US nephrology fellowship programs. Our study objective was to determine the association between nephrology training experience and subsequent clinical practice. We conducted a national survey of clinical nephrologists using a fax-back survey distributed between March 8, 2010 and April 30, 2010 (N = 629). The survey assessed the time distribution of clinical practice, self-assessment of preparedness to provide care for dialysis patients at the time of certification examination, distribution of dialysis modality among patients, and nephrologists' choice of dialysis modality for themselves if their kidneys failed. While respondents spent 28% of their time caring for dialysis patients, 38% recalled not feeling very well prepared to care for dialysis patients when taking the nephrology certification examination. Sixteen percent obtained additional dialysis training after fellowship completion. Only 8% of US dialysis patients use home dialysis; physicians very well prepared to care for dialysis patients at the time of certification or who obtained additional dialysis training were significantly more likely to provide care to home peritoneal dialysis patients. Even though 92% of US dialysis patients receive thrice weekly in-center hemodialysis, only 6% of nephrologists selected this for themselves; selection of therapy for self was associated with dialysis modalities used by their patients. Nephrology training programs need to ensure that all trainees are very well prepared to care for dialysis patients, as this is central to nephrology practice. Utilization of dialysis therapies other than standard hemodialysis is dependent, in part, on training experience.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".