The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) longitudinal study protocol: Phase 4 (“Enrichment”) and Phase 5 (“Rescan”)
Bibliographic record
Abstract
Abstract Background The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) started in 2010 to study the effect of healthy adult ageing on cognition and the brain in a population-derived sample. The study design and protocol for Phases 1-3 of Cam-CAN were detailed in Shafto et al. (2014); this paper outlines the design and protocol of Phases 4–5, which enable longitudinal investigation of cognitive and brain ageing over approximately 12 years. More details about the Cam-CAN project can be found here: www.cam-can.org . Methods/Design Phase 4 was an at-home assessment of cognition, demographics and lifestyle, performed approximately 6 years after Phase 1 (baseline assessment), for which all people from Phase 1 were invited. Phase 5 combined repeated online cognitive, demographics and lifestyle assessment, followed by in-lab attendance for MRI and MEG brain scanning, approximately 12 years after Phase 1, for which all people from Phase 2 (baseline brain assessment) were invited. Demographics, lifestyle and cognitive data are therefore now available for three timepoints, and MRI and MEG brain data for two timepoints. Discussion The Cam-CAN study offers deep and wide phenotyping of neurocognitive health across the adult lifespan (18-96). These rich data will allow researchers to address questions like: why do some people maintain their cognitive abilities better than others, in terms of their brain structure or function, their lifestyle and/or their genetics? Given the shifting demographics towards old age in most countries, this knowledge will be important to help people function independently for longer, reducing both individual and societal burden.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.104 | 0.035 |
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".