Executive functions in frontotemporal dementia and Lewy body dementia.
Bibliographic record
Abstract
Diagnosis of different types of dementia is often based on clinical symptomatology rather than underlying pathology; therefore, accurate diagnosis depends on a thorough description of cognitive functioning in different dementias. Furthermore, direct comparison of cognitive functions between different types of dementia is necessary for differential diagnosis. Executive dysfunction is common in several types of dementia, including frontotemporal dementia (FTD) and Lewy body dementia (LBD); however, FTD and LBD patients have never been directly compared on measures of executive functioning. The authors compared the performance of 17 FTD and 15 LBD patients on 6 measures of executive functioning in terms of statistical group differences, mean severity of clinical impairment in comparison to normal controls, and frequency of impairment. Results indicated a remarkably similar pattern of performance across all areas examined in terms of mean performance, as well as degree and frequency of impairment. Only the Stroop test produced results that could potentially differentiate the patient groups. These findings suggest that both FTD and LBD should be considered disorders involving executive dysfunction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".