Reengineering Dialysis: The Role of Palliative Medicine
Bibliographic record
Abstract
BACKGROUND: End-stage renal disease is a life-limiting illness associated with significant morbidity. Half of all individuals with end-stage renal disease are unable to participate in decision making at the end of life, which makes advance care planning critical in this population. OBJECTIVE: We sought to determine the feasibility of embedding palliative medicine consultations in the hemodialysis unit during treatment runs and the impact of this intervention on advance care planning and symptom management. DESIGN: Single-center, prospective cohort study. SETTING/SUBJECTS: Adults receiving in-center hemodialysis at a single outpatient unit were eligible. All consultations occurred during the patients' hemodialysis runs between January 1 and June 30, 2012. MEASUREMENT: Medical records were reviewed for documentation of advance directives, resuscitation status, and goals of care discussions before and after palliative medicine intervention. Symptom surveys with the Modified Edmonton Symptom Assessment Scale (validated for end-stage renal disease) were performed preintervention and postintervention. RESULTS: Ninety-two patients were eligible; 91 underwent palliative medicine consultation. Symptoms were well controlled at baseline prior to any intervention. After palliative medicine consultation, the prevalence of unknown code status decreased from 23% to 1% and goals of care documentation improved from 3% to 59%. CONCLUSION: Palliative medicine consultation during in-center outpatient hemodialysis was well received by patients and clinical staff. Patients' symptoms were well managed at baseline by the primary nephrology team. The frequency of goals of care documentation and clarification of code status improved significantly. Embedded palliative medicine specialists on the dialysis care team may be effective in improving multidisciplinary patient-centered care for patients with end-stage renal disease.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".