Is anemia associated with cognitive impairment and delirium among older acute surgical patients?
Bibliographic record
Abstract
AIM: The determinants of cognitive impairment and delirium during acute illness are poorly understood, despite being common among older people. Anemia is common in older people, and there is ongoing debate regarding the association between anemia, cognitive impairment and delirium, primarily in non-surgical patients. METHODS: Using data from the Older Persons Surgical Outcomes Collaboration 2013 and 2014 audit cycles, we examined the association between anemia and cognitive outcomes in patients aged ≥65 years admitted to five UK acute surgical units. On admission, the Confusion Assessment Method was carried out to detect delirium. Cognition was assessed using the Montreal Cognitive Assessment, and two levels of impairment were defined as Montreal Cognitive Assessment <26 and <20. Logistic regression models were constructed to examine these associations in all participants, and individuals aged ≥75 years only. RESULTS: A total of 653 patients, with a median age of 76.5 years (interquartile range 73.0-80.0 years) and 53% women, were included. Statistically significant associations were found between anemia and age; polypharmacy; hyperglycemia; and hypoalbuminemia. There was no association between anemia and cognitive impairment or delirium. The adjusted odds ratios of cognitive impairment were 0.95 (95% CI 0.56-1.61) and 1.00 (95% CI 0.61-1.64) for the Montreal Cognitive Assessment <26 and <20, respectively. The adjusted odds ratio of delirium was 1.00 (95% CI 0.48-2.10) in patients with anemia compared with those without. Similar results were observed for the ≥75 years age group. CONCLUSIONS: There was no association between anemia and cognitive outcomes among older people in this acute surgical setting. Considering the retrospective nature of the study and possible lack of power, findings should be taken with caution. Geriatr Gerontol Int 2018; 18: 1025-1030.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".