Peer Review #2 of "Dementia-related user-based collaborative filtering for imputing missing data and generating a reliability scale on clinical test scores (v0.2)"
Bibliographic record
Abstract
Medical doctors may struggle to diagnose dementia, particularly when clinical test scores are missing or incorrect.In case of any doubts, both morphometrics and demographics are crucial when examining dementia in medicine.This study aims to impute and verify clinical test scores with brain MRI analysis and additional demographics, thereby proposing a decision support system that improves diagnosis and prognosis in an easy-to-understand manner.Therefore, we impute the missing clinical test score values by unsupervised dementia-related user-based collaborative filtering to minimize errors.By analyzing succession rates, we propose a reliability scale that can be utilized for the consistency of existing clinical test scores.The complete base of 816 ADNI1-Screening samples was processed, and a hybrid set of 603 features was handled.Moreover, the detailed parameters in use, such as the best neighborhood and input features were evaluated for further comparative analysis.Overall, certain collaborative filtering configurations outperformed alternative state-of-the-art imputation techniques.The imputation system and reliability scale based on the proposed methodology are promising for supporting the clinical tests.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.024 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.188 | 0.118 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".