Documentation of cognitive impairment screening amongst older hospitalised Australians: a prospective clinical record audit
Bibliographic record
Abstract
BACKGROUND: Failure to detect cognitive impairment (CI) in hospitalised older inpatients has serious medical and legal implications, including for the implementation of care planning. This mixed methods study aimed to determine amongst hospital in-patients aged ≥ 65 years: (1) Rates of documentation of screening for CI, including the factors associated with completion of screening; (2) Rates of undocumented CI amongst patients who had not received screening during their admission; (3) Healthcare provider practices and barriers related to CI screening. METHODS: A mixed methods study incorporating a clinical audit and interviews with healthcare providers was conducted at one Australian public hospital. Patients were eligible for inclusion if they were aged 65 years and older and were admitted to a participating ward for a minimum of 48 h. Patient characteristics, whether CI screening had been documented, were extracted using a template. Patients who had not been screened for CI completed the Montreal Cognitive Assessment (MoCA) to determine cognitive status. Interviews were conducted with healthcare providers to understand practices and barriers to screening for CI. RESULTS: Of the 165 patients included, 34.5% (n = 57) had screening for CI documented for their current admission. Patients aged > 85 years and those with two or more admissions had greater odds of having CI screening documented. Among patients without CI screening documented, 72% (n = 78) were identified as cognitively impaired. While healthcare providers agreed CI screening was beneficial, they identified lack of time and poor knowledge as barriers to undertaking screening. CONCLUSIONS: CI is frequently unrecognised in the hospital setting which is a missed opportunity for the provision of appropriate care. Future research should identify feasible and effective strategies to increase implementation of CI screening in hospitals.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".