No Rest for the Wicked: The Complexity of Nephrology Inpatients Is Increasing over Time
Bibliographic record
Abstract
Background: Patients seen by nephrologists are known to be more complex than those seen by other medical specialties. Anecdote suggests that the complexity of nephrology inpatients has increased over time, but this has not been studied. We assessed temporal trends in the complexity of inpatients seen by nephrologists. Methods: We did a retrospective cohort study of all adults in Alberta, Canada who were seen by one or more nephrologists during a hospitalization between 2011-2020. We used validated algorithms applied to population-based administrative data to assess patient characteristics. We evaluated 10 markers of complexity; 8 were measured in the year prior to admission to minimize the competing risk of mortality (presence of ≥10 comorbidities, >15 prescribed drugs, presence of a mental health condition, presence of frailty, number of physician specialties involved in care, number of individual physicians involved in care, ≥2 prior hospitalizations, and >5 emergency visits). We assessed all-cause death and placement in long-term care (LTC) during the year following admission. Differences over time were assessed using generalized linear models. Results: Among 45,156 inpatients, median age remained stable at 65y over 2011-2020. The table shows the secular changes in the 10 complexity markers. The proportion with a mental health condition and the likelihood of placement in LTC remained stable, whereas the risk of mortality decreased. Complexity as assessed by the remaining 7 markers increased over time, often to a substantial extent. For example, the proportion of patients with ≥10 comorbidities increased by 23%, whereas the proportion with frailty increased by 51%. Trends were similar after adjustment for age (data not shown). Conclusion: The complexity of nephrology inpatients is increasing over time and is not explained by population aging. The secular decrease in mortality is encouraging and warrants further investigation. These findings have implications for workforce planning, nephrology training programs and physician reimbursement policies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".