Dedicated Patient Optimization Program Improves Lives of Vulnerable Patients
Bibliographic record
Abstract
Background: Industrialized 'one size fits all' approaches to care and reliance on single intervention-based strategies results in negative outcomes for many. In response we developed and applied a systematic patient optimization program (POP) applying individualization and complex interventions to support the needs of the most vulnerable patients aiming to improve both the subjective experience and objective tolerability of dialysis. Methods: Patients (19) with inadequate response to conventional care were identified over a 12-month period. A series of comprehensive baseline objective (physiological, hemodynamic) and subjective assessments were performed and complex foundational/patient-disease specific interventions applied and rigorously evaluated over two weeks. Subjective impact was assessed using dynamic daily patient reported outcome measures. Objective assessments (inclusion and monitoring response) included continuous hemodynamic monitoring, tissue perfusion, vascular ultrasound and intradialytic echocardiography with assessment of HD-induced cardiac injury (myocardial stunning). Results: Nineteen patients, largely with issues related to hypotension & failure to achieve target weight- in concert with a range of negative symptoms. All patients had multiple components of dialysis altered. POP was associated with a marked improvement in a range of subjective and objective outcomes. QOL improved by 20% (p0.02), recovery time by 86% (p0.005). Reductions in intradialytic systolic BP improved by 19% (p<0.01), interdialytic weight gain 33% (p0.01) and myocardial stunning was significantly reduced (by 63% p0.01) with tight association between changes in BP (p<0.001) and ultrafiltration (p<0.05). At inclusion all patients had been declined transplantation listing, after re-evaluation 3/19 were listed and two successfully received graft. Conclusion: A complex intervention delivered in a systematic POP improves the subjective wellbeing and objective tolerability of HD in the most challenging 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".