The role of advanced practice providers in the growing nephrologist shortage
Bibliographic record
Abstract
Globally, there is a growing shortage of healthcare professionals working in the nephrology specialty, especially physicians. The issue is two-fold: an expanding population of patients with renal diseases and fewer physicians applying to nephrology fellowship programs to care for them.1 Advanced practice providers (APPs) such as physician assistants (PAs) and nurse practitioners (NPs) play a crucial role in the healthcare team, delivering similar services as their physician counterparts. Not only can APPs fill the gaps in physician shortages, but they can contribute to improvement in the emotional well-being of the entire healthcare team. One observational cross-sectional study2 of 420 family medicine clinicians found that teams containing physicians and APPs were associated with lower levels of burnout. Including APPs on healthcare teams can also impact care quality and cost efficiency. Another retrospective cohort study3 by Roy et al. evaluating approximately 5200 records of patients admitted to a hospital medicine floor found that patients co-managed by PAs and physicians had 3.9% lower total healthcare costs (95% Confidence Interval [CI], -7.5% to -0.3%, p < 0.05) with no significant difference in health outcomes compared to patients managed by teams of only physicians. Additionally, APP postgraduate residencies can assist in generating providers with adequate clinical knowledge and skills to care for patients. A comparative analysis4 found that inpatients co-managed by hospitalists and PAs in a 2-year postgraduate training program had lower all-cause mortality than those co-managed by hospitalists and residents in a traditional 3-year program (1.94% vs 2.85%, P < 0.001). Despite these findings, a lack of research regarding APPs in nephrology exists. This thesis aims to illustrate the nephrologist shortage, the expanding complexity of the patient population requiring kidney care, and how APPs can effectively fill this growing need.--Author's abstract
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".