TEMPORALLY GRADED SEMANTIC MEMORY LOSS IN ALZHEIMER'S DISEASE: CROSS-SECTIONAL AND LONGITUDINAL STUDIES
Bibliographic record
Abstract
Semantic knowledge of famous names and words that entered popular North American culture at different times in the 20th century was examined in 16 patients with mild-to-moderate Alzheimer's disease (AD), 12 of whom were re-tested 1 year later. All patients showed evidence of temporally graded memory loss, with names and words from the remote past being relatively better preserved than recent names and words. There was considerable between-patient variability with respect to severity of semantic impairment. Most patients exhibited losses extending back 30-40 years; however, two mildly impaired (MMSE >28) patients showed deficits restricted to the last 10-15 years. At the 1-year follow-up, patients not only exhibited more severe deficits overall, but the temporally graded period of loss extended further back in time, suggesting that this deficit reflects a loss of previously intact knowledge and not merely faulty encoding or lack of exposure to the material. The extensive period of graded semantic loss exhibited by most patients contrasts with the temporally limited retrograde semantic loss typical of medial temporal lobe amnesia. We propose that short periods of temporally graded semantic memory loss can be explained by damage to medial temporal structures, but that extensive periods of graded loss occur only with additional damage to neocortical tissue. This pattern contrasts with that of autobiographical memory loss, which is often ungraded and extends for the person's entire lifetime, even when damage is restricted to the medial temporal lobes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".