Bibliographic record
Abstract
Abstract Neurodegenerative diseases, such as Alzheimer and Parkinson’s, are distinguished by the progressive loss of motor capabilities, cognitive abilities, or both. The specific origins of neurodegenerative disorders are often unclear; however, they can include genetic, environmental, and lifestyle factors, and are scientifically interpreted as a breakdown of brain and spinal cord functioning. Treatment often focuses on symptom management, as there is presently no cure for these disorders, only treatments to delay disease progression and improve patients’ quality of life. An early diagnosis is crucial to initiate treatment promptly since significant and irreversible damage may have already occurred once symptoms manifest. It is widely acknowledged that one of the initial skills affected by cognitive disorders is handwriting, which relies on a combination of kinesthetic and motor-perceptive abilities. Significant progress has been achieved in this field, beginning with establishing various handwriting protocols that outline the specific writing or drawing tests to be conducted. Nevertheless, it’s important to highlight that there is no universal consensus regarding the quantity and nature of tasks to be utilized. Additionally, there is a scarcity of standardized databases that gather this kind of data, which typically only refers to a relatively small number of participants. This aspect adds another layer of complexity in the realm of machine learning techniques, which typically demand substantial volumes of data. Moreover, there is a lack of consensus regarding the specific features researchers should prioritize. Indeed, the challenge of identifying effective features that enable the system to differentiate between regular age-related handwriting changes and those induced by neurodegenerative disorders remains unresolved. This paper will investigate existing studies focusing on detecting Alzheimer’s disease using handwriting analysis. Our examination will encompass the databases employed, the features extracted, the methodologies applied, and the ultimate discoveries and conclusions drawn from these studies.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".