Longitudinal detection of dementia through lexical and syntactic changes in writing: a case study of three British novelists
Bibliographic record
Abstract
We present a large-scale longitudinal study of lexical and syntactic changes in language in Alzheimer's disease using complete, fully parsed texts and a large number of measures, using as our subjects the British novelists Iris Murdoch (who died with Alzheimer's), Agatha Christie (who was suspected of it), and P.D. James (who has aged healthily). We avoid the limitations and deficiencies of Garrard et al.'s [(2005), The effects of very early Alzheimer's disease on the characteristics of writing by a renowned author. Brain, 128 (2): 250–60] earlier study of Iris Murdoch. Our results support the hypothesis that signs of dementia can be found in diachronic analyses of patients’ writings, and in addition lead to new understanding of the work of the individual authors whom we studied. In particular, we show that it is probable that Agatha Christie indeed suffered from the onset of Alzheimer's while writing her last novels, and that Iris Murdoch exhibited a ‘trough’ of relatively impoverished vocabulary and syntax in her writing in her late 40s and 50s that presaged her later 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".