Predictors for nephrology outpatient care and recurrence of acute kidney injury (<scp>AKI</scp>) after an in‐hospital <scp>AKI</scp> episode
Bibliographic record
Abstract
Acute kidney injury (AKI) is associated with increased long-term risk of end-stage kidney disease (ESKD) and mortality. Nephrology care following discharge from hospital may improve survival through prevention of recurrent AKI events. In this study, we examined the factors that were associated with outpatient nephrology follow-up after the development of AKI on patients who had a nephrology in-hospital consultation and were discharged from McGill University Health Centre between January 1, 2006 and December 31, 2010. The associated factors for AKI-free survival postdischarge were assessed applying multivariate Cox hazard proportional models. Of 170 patients, only 22% of the AKI admissions studied were booked with nephrology follow-up after discharge. The unadjusted hazard ratio (HR) of outpatient nephrology care postdischarge was 1.82 (95% confidence interval [CI] 0.93-3.56) for AKI-free survival postdischarge. The adjusted HR was 2.04 (95% CI 1.01-4.12) when we adjusted for follow-up with other medical clinics, significant stage 4 and stage 5 chronic kidney disease and diabetes status. Patients with less comorbidities and higher serum creatinine on discharge received outpatient nephrology care. Nephrology outpatient care is associated with decreased risk of recurrence of AKI after discharge from hospital.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".