Predictors of survival in frontotemporal lobar degeneration syndromes
Bibliographic record
Abstract
After decades of research, large-scale clinical trials in patients diagnosed with frontotemporal lobar degeneration (FTLD) are now underway across multiple centres worldwide. As such, refining the determinants of survival in FTLD represents a timely and important challenge. Specifically, disease outcome measures need greater clarity of definition to enable accurate tracking of therapeutic interventions in both clinical and research settings. Multiple factors potentially determine survival, including the clinical phenotype at presentation; radiological patterns of atrophy including markers on both structural and functional imaging; metabolic factors including eating behaviour and lipid metabolism; biomarkers including both serum and cerebrospinal fluid markers of underlying pathology; as well as genetic factors, including both dominantly inherited genes, but also genetic modifiers. The present review synthesises the effect of these factors on disease survival across the syndromes of frontotemporal dementia, with comparison to amyotrophic lateral sclerosis, progressive supranuclear palsy and corticobasal syndrome. A pathway is presented that outlines the utility of these varied survival factors for future clinical trials and drug development. Given the complexity of the FTLD spectrum, it seems unlikely that any single factor may predict overall survival in individual patients, further suggesting that a precision medicine approach will need to be developed in predicting disease survival in FTLD, to enhance drug target development and future clinical trial methodologies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".