Assessment tools accuracy for classification and diagnosis of Primary Progressive Aphasia: A systematic review and meta-analysis protocol.
Bibliographic record
Abstract
Introduction. Primary Progressive Aphasia (PPA) is a syndrome characterized by progressive decline in language function. There are three main PPA syndromes, each one features different language profiles and neuropathologic substrates. Although there are current clinical diagnostic criteria for PPA categorization, the utility of these requires evaluation(s) by specialized staff and the administration of extensive cognitive batteries. A diagnostic tool for PPA is not currently standardized, though some batteries have been developed and/or validated exclusively for PPA categorization. We aim to describe which cognitive/aphasia diagnostic tool has the best accuracy for PPA diagnosis and categorization.
 Methods and Analysis. MEDLINE (PubMed), EMBASE and Web of Science databases will be searched using adequate search strategies. Studies including original data of possible, probable, and definite PPA cases according to current clinical diagnostic criteria for PPA will be included. Inclusion criteria will be 1) Studies describing data of a cognitive/aphasia clinical battery including at least one test measure (e.g., specificity, positive predictive values, etc.) and 2) PPA diagnosis according to current clinical criteria as the reference standard. Two reviewers will perform the screening and data extraction. Quality assessment will be performed according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) guidelines. This systematic review protocol is reported as stated by with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) 2015 statement.
 Dissemination. Findings of this systematic review protocol will be disseminated through a publication in a peer-reviewed journal. Results will be helpful to improve the diagnosis and classification of PPA syndromes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".