Seasonal pattern of incidence and outcome of Acute Kidney Injury: A national study of Welsh AKI electronic alerts
Bibliographic record
Abstract
OBJECTIVES: To identify any seasonal variation in the occurrence of, and outcome following Acute Kidney Injury. METHODS: The study utilised the biochemistry based AKI electronic (e)-alert system established across the Welsh National Health Service to collect data on all AKI episodes to identify changes in incidence and outcome over one calendar year (1st October 2015 and the 30th September 2016). RESULTS: There were total of 48 457 incident AKI alerts. The highest proportion of AKI episodes was seen in the quarter of January to March (26.2%), and the lowest in the quarter of October to December (23.3%, P < .001). The same trend was seen for both community-acquired and hospital-acquired AKI sub-sets. Overall 90 day mortality for all AKI was 27.3%. In contrast with the seasonal trend in AKI occurrence, 90 day mortality after the incident AKI alert was significantly higher in the quarters of January to March and October to December compared with the quarters of April to June and July to September (P < .001) consistent with excess winter mortality reported for likely underlying diseases which precipitate AKI. CONCLUSIONS: In summary we report for the first time in a large national cohort, a seasonal variation in the incidence and outcomes of AKI. The results demonstrate distinct trends in the incidence and outcome of AKI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".