Under Reporting of Dementia Deaths on Death Certificates: A Systematic Review of Population-based Cohort Studies
Bibliographic record
Abstract
The purpose of this review is to assess the extent to which dementia is omitted as a cause of death from the death certificates of patients with dementia. A systematic literature search was performed to identify population-based cohort studies in which all participants were examined or screened for symptoms of dementia with a validated instrument followed by confirmation of any suspected cases with a clinical examination (two-phase investigation). Data were extracted in a standardized manner and assessed through the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative. Seven studies met the selection criteria. These were from the Americas (5 articles: 2 from Canada, 2 from the US, and 1 from Brazil) and Europe (2 articles: 1 from the UK and 1 from Spain). Each met at least 83% of the STROBE criteria. The reporting of dementia on death certificates was poor in these 7 studies, ranging from 7.2%-41.8%. Respiratory or circulatory-related problems were the most frequently reported causes of death among people who were demented but who were not reported as demented on death certificates. The use of death certificates for studying dementia grossly underestimates the occurrence of dementia in the population. The poor reporting of dementia on these certificates suggests a lack of awareness of the importance of dementia as a cause of death among medical personnel. There is an urgent need to provide better education on the importance of codification of dementia on death certificates in order to minimize errors in epidemiological studies on dementia.
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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 | 0.048 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".