The semantic storage loss score: An Algorithm for measuring an individual's level of semantic storage loss due to temporal lobe damage in neurodegenerative disease
Bibliographic record
Abstract
Anomia is common in Primary Progressive Aphasia (PPA), and there is considerable evidence that semantic problems (as opposed to impaired access to output word phonology) exist in many PPA individuals irrespective of their strict subtype, including a loss of representations from semantic memory, which is typical for people with the semantic variant of PPA. In this manuscript we present a straightforward novel clinical algorithm that quantifies this degree of semantic storage impairment. We sought to produce an algorithm by employing tasks that would measure key elements of semantic storage loss: a) whether an unrecalled name could be retrieved with cues; b) if performance for items was consistent across tasks; and c) the degree to which a participant's performance was related to general severity of cognitive impairment rather than semantic loss. More specifically, these tasks were given to 28 individuals with PPA (12 participants had a clinical diagnosis of atypical Alzheimer's Disease with the logopenic variant of PPA; the remaining 16 participants received a clinical diagnosis of Frontotemporal dementia (11 were classified as the non-fluent variant of PPA and five were the semantic variant of PPA). Scores from these tasks produced a single omnibus semantic memory storage loss score (SSL score) for each person that ranged from 0.0 to 1.0, with scores closer to 0 more indicative of semantic storage loss. Indeed, supporting the hypothesis that our scores measure the degree of semantic storage loss, we found participants with the semantic variant of PPA had the lowest scores, and SSL scores could predict the degree of hypometabolism in the anterior temporal lobe; even when only people with the logopenic variant of PPA were examined. Thus, these scores show promise quantitating the degree of a person's semantic representation loss.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".