81UNDER-DIAGNOSED COGNITIVE IMPAIRMENT IN RENAL INPATIENTS - A SINGLE CENTRE QIP EXPERIENCE
Bibliographic record
Abstract
Cognitive impairment is highly prevalent in patients with chronic kidney disease (CKD), with up to 86% prevalence in haemodialysis (HD) patients [1, 2]. It is a risk factor for prolonged hospitalisation, readmission and mortality [3, 4]. Responding to a Commissioning for Quality and Innovation incentive to identify cognitive impairment in elderly inpatients, we undertook a Quality Improvement Project (QIP) to characterise the presence of cognitive dysfunction in renal inpatients. We prospectively collected clinical data on 33 randomly selected renal inpatients ≥ 65 years old. We recorded demographics, history of cognitive impairment, a Charlson Comorbidity Index (CCI) and a CSHA clinical frailty scale. We performed an Abbreviated Mental Test Score (AMTS) and Montreal Cognitive Assessment (MOCA). DSM-IV criteria were used to define delirium. A 2-cycle QIP was undertaken. The interventions were education at the departmental induction and the introduction of a forcing function for the discharge summary. Data was performed using Microsoft Excel. Mean patient age was 76, 70% male, mean length of stay (LOS) 12 days and 16 were HD patients. Following the QIP all patients had a statement regarding cognitive function on their discharge summary. Only 1patient (3%) had a prior diagnosis of dementia. Mean MOCA score was 23 ± 5.5 and AMTS 8.5 ± 1.8.The number of patients with either a significant AMTS or MOCA was 43% and 54% of these patients had delirium. Given the high prevalence of cognitive impairment in HD and CKD patients, there were fewer renal patients with a prior diagnosis of dementia than expected. Moreover, we found a high level of undiagnosed delirium which confers a morbidity and mortality burden. Of note the CSHA frailty scale and CCI did not correlate with worse AMTS or MOCA results, suggesting that cognitive impairment could not be predicted by frailty and multi-morbidity and further research is required to understand the reasons for this.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".