Economic evaluations of strategies targeting pre-diagnosis dementia populations: Protocol for a systematic review
Bibliographic record
Abstract
Introduction: Dementia remains incurable, and treatment trials are typically conducted after the symptoms manifest, potentially too late in the disease process to alter its course. Thus, it is essential to identify and implement cost-effective strategies targeting individuals who have not yet been formally diagnosed with dementia. This review aims to scrutinise emerging evidence and present a comprehensive summary of cost-effectiveness estimates of all strategies targeting the pre-diagnosis dementia populations. Method and analysis: A systematic search will be conducted across six electronic databases. All articles will be assessed against pre-defined eligibility criteria through title and abstract screening, and full-text screening phases. Data from the included articles will be extracted using a standardized template. A newly established framework based on the CHEERS 2022 checklist will be applied to assess the reporting quality of the included articles. The entire review process, from screening to data extraction and quality assessment, will be a dual process conducted by two reviewers. Disagreements will be resolved by a third senior reviewer. The extracted data will be synthesised and presented in tables and figures. Conclusion: This systematic review will present evidence of cost-effectiveness, along with the strengths and limitations of the existing literature. These findings aim to identify existing gaps, thereby informing and guiding the design of future studies in this domain. Ethics and dissemination: Since this is a systematic review protocol, ethical approval is not required. The results will be published in a peer-reviewed journal, with both raw and summarised data shared through the journal or other open platforms. Systematic review registration: PROSPERO - CRD42024521521.
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 | 0.096 | 0.145 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.019 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.015 |
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".