Immune mechanisms as predictors of cognitive impairment and therapeutic targets in pre-symptomatic Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is commonly known for the typical memory loss that accompanies its clinical expression.However, it is now understood that the clinical expression of the disease is most likely preceded by up to three decades of pathological changes.Thus, the lack of efficacy of drugs developed and tested in the last twenty years may owe, at least in part, to treatment being "too little too late."The period of silent pathological changes, otherwise called the pre-symptomatic phase, offers a window of opportunity to identify biological mechanisms altered in the pathogenetic process and for their modification through preventive interventions.a PhD.He gave me the freedom to work independently and pursue my own research questions while fostering scientific rigor and considerably improving my writing skills.His advice and lessons will undoubtedly continue to help me throughout my career.I would also like to thank my co-supervisor, Dr. Sylvia Villeneuve for taking me into her lab and giving me a rich, stimulating and safe environment to pursue my graduate studies.Beyond teaching me how to do great science, she also leads by example in creating an outstanding environment for all trainees inside and outside of the lab.Within the Villeneuve lab, I would like to acknowledge Dr. Etienne Vachon-Presseau who's acute critical mind leads to better scientific practice.I hope I have learned a lot from him over the last couple of years.I would also like to thank fellow graduate student Alexa Pichet Binette for her enthusiasm, positivity and constructive criticism which helps others to continually improve.I am grateful for Dr. Julie Gonneaud sharing her expertise and knowledge of our field and also her pastries which invariably contributed to better days.I would also like to acknowledge Hazal Ozlen, Melissa McSweeney, Dr. Theresa Köbe
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".