Acute kidney injury in cats and dogs: A proportional meta-analysis of case series studies
Bibliographic record
Abstract
INTRODUCTION: Risk of mortality in the setting of acute kidney injury (AKI) in cats and dogs remains unclear. OBJECTIVES: To evaluate the incidence of mortality in cats and dogs with AKI based on etiology (i.e. infectious versus non-infectious; receiving dialysis versus conservative treatment). MATERIALS AND METHODS: Ovid Medline, EMBASE, and LILACS were searched up to July 2016. Articles were deemed eligible if they were case series studies evaluating the incidence of all-cause mortality in cats and dogs with AKI, regardless of etiology or the nature of treatment. RESULTS: Eighteen case series involving 1,201animalsproved eligible. The pooled proportions for overall mortality were: cats53.1% [95% CI 0.475, 0.586; I2 = 11,9%, p = 0.3352]; dogs 45.0% [95% CI 0.33, 0.58; I2 = 91.5%, P < 0.0001]. A non-significant increase in overall mortality risk was found among dialysed animals relative to those managed with conservative treatment, independent of animal type and the etiology of their AKI. The pooled proportions for overall mortality according to etiology, regardless of treatment type, were: AKI due infectious etiology for cats and dogs, 19.2% [95% CI 0.134, 0.258; I2 = 37.7%, P = 0.0982]; AKI due non-infectious etiology for cats and dogs, 59.9% [95% CI 0.532, 0.663; I2 = 51.0%, P = 0.0211]. CONCLUSION: Our findings suggest higher rates of overall mortality in cats and dogs with AKI due to non-infectious etiologies relative to infectious etiologies, and showed non-significant differences in terms of higher rates associated with dialysis compared to conservative management. Further investigations regarding optimal time to initiate dialysis and the development of clinical models to prognosticate the course of disease and guide optimal treatment initiation for less severe cases of AKI in cats and dogs is warranted.
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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 | 0.036 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".