Verbal and non‐verbal auditory comprehension tests in early diagnosis of Alzheimer's disease
Bibliographic record
Abstract
Abstract Background The purposes of this study are to correlate auditory comprehension impairment with AD early development and to evaluate the verbal and non‐verbal auditory comprehension tests for early diagnosis of AD. Method The 24 healthy controls (HC), 30 amnestic mild cognitive impairment (aMCI) and 27 mild AD (mAD) patients were recruited. The verbal (yes‐or‐no question, listening identification and oral instruction) and non‐verbal (sound categorization, emotion judgment, beat judgement, sound naming) auditory comprehension tests were adapted to evaluate the diagnostic ability of auditory comprehension. The receiver operating characteristic curves were used to analyze the diagnostic values. Result All tests except for yes‐or‐no question presented moderate or higher level of positive correlation in the scores with Mini‐Mental State Examination (MMSE) and Montreal Cognitive Assessment (MOCA), among which sound naming and oral instruction were the best. Statistical analysis identified a significant difference of sound naming and verbal auditory comprehension tests in aMCI group compared with the HC group. In addition, the mAD group exhibited statistically significant difference in sound categorization, sound naming, beat judgement and oral instruction when compared with the other two groups. Oral instruction sound categorization and naming benefited better in early diagnosis than the other subtests. Conclusion The impairments of auditory comprehension emerge early in AD, or even aMCI patients, which worsen with the progression of the disease. The tests of sound categorization, sound naming and oral instruction exhibited better discriminative values, which might be beneficial for the early diagnosis of AD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".