Word and phrase duration in Mandarin-speaking individuals with Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by irreversible cognitive deterioration, often manifesting in pathological speech patterns [Baker et al., Clin. Linguist. Phon. 21(11–12), 859–867 (2007)]. Previous research has examined temporal and acoustic features of AD patients’ speech, including pitch, volume, and voice quality, showing that these measures can be used to discriminate between people with AD and healthy older adults [Meilan et al., Dement. Geriatr. Cogn. Disord. 37(5–6), 327–334 (2014)]. However, whether word and phrase duration are affected by AD remains unclear. The present study measured word and phrase duration from nine Mandarin-speaking AD patients and nine gender-matched neurotypical controls undertaking picture description and naming tasks. Preliminary results show AD patients exhibited a significant difference (t (1688.1) = −5.88, p < .001) in word duration compared to controls, with shorter word duration in the naming task (t (1088.8) = −8.66, p < .001) and longer in the picture description task (t (484.13) = 2.40, p = 0.017); AD patients also uttered significantly shorter phrases than controls in the picture description task (t (86.01) = −2.17, p = 0.033). The preliminary result suggests that AD may affect word and phrase duration. [Work supported by NIH and NSERC].
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".