A Survey of Canadian Nephrologists Assessing Prognostication in End-Stage Renal Disease
Bibliographic record
Abstract
BACKGROUND: Patients with end-stage renal disease (ESRD) frequently have a relatively poor prognosis with complex care needs that depend on prognosis. While many means of assessing prognosis are available, little is known about how Canadian nephrologists predict prognosis, whether they routinely share prognostic information with their patients, and how this information guides management. OBJECTIVE: To guide improvements in the management of patients with ESRD, we aimed to better understand how Canadian nephrologists consider prognosis during routine care. DESIGN AND METHODS: A web-based multiple choice survey was designed, and administered to adult nephrologists in Canada through the e-mail list of the Canadian Society of Nephrology. The survey asked the respondents about their routine practice of estimating survival and the perceived importance of prognostic practices and tools in patients with ESRD. Descriptive statistics were used in analyzing the responses. RESULTS: Less than half of the respondents indicated they always or often make an explicit attempt to estimate and/or discuss survival with ESRD patients not on dialysis, and 25% reported they do so always or often with patients on dialysis. Survival estimation is most frequently based on clinical gestalt. Respondents endorse a wide range of issues that may be influenced by prognosis, including advance care planning, transplant referral, choice of dialysis access, medication management, and consideration of conservative care. LIMITATIONS: This is a Canadian sample of self-reported behavior, which was not validated, and may be less generalizable to non-Canadian health care jurisdictions. CONCLUSIONS: In conclusion, prognostication of patients with ESRD is an important issue for nephrologists and impacts management in fairly sophisticated ways. Information sharing on prognosis may be suboptimal.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".