Annual wellness visits and care management before and after dialysis initiation
Bibliographic record
Abstract
INTRODUCTION: Demands of dialysis regimens may pose challenges for primary care provider (PCP) engagement and timely preventive care. This is especially the case for patients initiating dialysis adjusting to new logistical challenges and management of symptoms and existing comorbid conditions. Since 2011, Medicare has provided coverage for annual wellness visits (AWV), which are primarily conducted by PCPs and may be useful for older adults undergoing dialysis. METHODS: We used the OptumLabs® Data Warehouse to identify a cohort of 1,794 Medicare Advantage (MA) enrollees initiating dialysis in 2014-2017 and examined whether MA enrollees (1) were seen by a PCP during an outpatient visit and (2) received an AWV in the year following dialysis initiation. RESULTS: In the year after initiating dialysis, 93 % of MA enrollees had an outpatient PCP visit but only 24 % received an annual wellness visit. MA enrollees were less likely to see a PCP if they had Charlson comorbidity scores between 0 and 5 than those with scores 6-9 (odds ratio (OR) = 0.59, 95 % CI: 0.37-0.95), but more likely if seen by a nephrologist (OR = 1.60, 95 % CI: 1.01-2.52) or a PCP (OR = 15.65, 95 % CI: 9.26-26.46) prior to initiation. Following dialysis initiation, 24 % of MA enrollees had an AWV. Hispanic MA enrollees were less likely (OR = 0.57, 95 % CI: 0.39-0.84) to have an AWV than White MA enrollees, but enrollees were more likely if they initiated peritoneal dialysis (OR = 1.54, 95 % CI: 1.07-2.23) or had an AWV in the year before dialysis initiation (OR = 4.96, 95 % CI: 3.88-6.34). CONCLUSIONS: AWVs are provided at low rates to MA enrollees initiating dialysis, particularly Hispanic enrollees, and represent a missed opportunity for better care management for patients with ESKD. Increasing patient awareness and provider provision of AWV use among dialysis patients may be needed, to realize better preventive care for 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".