Bibliographic record
Abstract
Problem: Alzheimer disease (AD) is defined as “an irreversible, progressive brain disorder that slowly destroys memory and thinking skills” (National Institute on Aging) and is pathologically characterized by abnormal deposition of neurofibrillary tangles and amyloid plaques. However, these abnormal protein aggregates also accumulate with aging, which complicates the distinction between aging and AD. Results: This presentation will discuss the concepts of disease and then compare and contrast these with the definition of Alzheimer disease. It briefly discusses causality and examines how associations have to be conflated with causality in the pathological diagnoses of neurodegenerative diseases. It also indicates some inherent biases that pathologists have in identifying disease and the pathological changes resulting from diseases. The presentation will present examples from the Calgary Brain Bank of patients without known neurodegenerative disease who die at different ages, as well as different pathological presentations of neurofibrillary tangles and amyloid plaques. Several known causes of AD will be reviewed and contrasted with what is commonly considered “normal aging”. Discussion: This presentation argues that Alzheimer disease pathology represents a final common pattern of changes that results from several or possibly many different aetiologies. Recognizing that these changes have several different causes might better guide future research into late onset dementias. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Consider observational biases used in the diagnoses of different dementias 2. Distinguish several aetiologies of Alzheimer-type Neuropathology 3. Contemplate how neuropathology has done a disservice in dementia research by focusing on accumulations of abnormal proteins
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".