A Pilot Study of the Serious Illness Conversation Guide in a Dialysis Clinic
Bibliographic record
Abstract
Clinician-led conversations about future care priorities occur infrequently with end-stage renal disease (ESRD) patients on dialysis. This was a pilot study of structured serious illness conversations using the Serious Illness Conversation Guide (SICG) in a single dialysis clinic to assess acceptability of the approach and explore conversation themes and potential outcomes among patients with ESRD. Twelve individuals with ESRD on dialysis from a single outpatient dialysis clinic participated in this study. Participants completed a baseline demographics survey, engaged in a clinician-led structured serious illness conversation, and completed an acceptability questionnaire. Conversations were recorded, transcribed and thematically analyzed. The average age of participants was 68.8 years. The conversations averaged 20:53 in length. Ten participants (83%) felt that the conversation was held at the right time in their clinical course and eleven participants (91%) felt that it was worthwhile. Most participants (73%) reported neutral feelings about clinician use of a printed guide. Eleven participants (91%) reported no change in anxiety about their illness following the conversation, and five participants (42%) reported that the conversation increased their hopefulness about future quality of life. Thematic analysis revealed common perspectives on dialysis including that participants view in-center hemodialysis as temporary, compartmentalize their kidney disease, perceive narrowed life experiences and opportunities, and believe dialysis is their only option. This pilot study suggests that clinician-led structured serious illness conversations may be acceptable to patients with ESRD on dialysis. The themes identified can inform future serious illness conversations with dialysis patients.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".