Diagnosis and Treatment of Dementia: the Fourth Canadian Consensus Conference
Bibliographic record
Abstract
It is inevitable that when discoveries are made in any medical fi eld there is pressure to move expeditiously to clinical applications of these discoveries. While there is every reason to disseminate widely the results of effi cacious therapeutic trials that improve meaningful clinical outcomes, and diagnostic strategies that are more sensitive, more specifi c, less burdensome to the patient and more parsimonious of resources, some restraint is advisable when the benefi ts are less compelling. For example, a technique that allows an earlier diagnosis of an incurable disease – such as Alzheimer’s disease (AD) – risks labeling an individual, aff ecting the person’s ability to acquire life, health or travel insurance, and may result in suspension of driving privileges. Finding the balance between embracing leading-edge technologies prematurely and failing to accept proven therapies or diagnostic strategies in a timely manner is the sweet spot to which we should all aspire. We must try to avoid repeating the unfortunate experiences occasioned by widespread prescription of medications before the true range of adverse eff ects has been elucidated (for example, rofecoxib), by surgical procedures of dubious value (for example, external carotid artery to internal
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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 | 0.049 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".