Poster Presentations at the 8th Canadian Conference on Dementia (CCD), Ottawa, October 2015
Bibliographic record
Abstract
An 82 year lady came to my office, with a 2-year history of difficulty in walking.The referring geriatrician/memory clinic specialist called me saying that the patient couldn't walk, though she had good strength.She simply couldn't make her arms and legs do what she wanted them to do.The family said that it was almost like forgetting to walk.Patient had difficulty with dressing, using crockeries and cutleries, writing, and in combing her hair.She also said that her right arm often goes into odd positions.There was no significant past history or family history of any neurodegenerative disorder.Neurological examination showed an alert, cooperative woman who scored 27 on Mini-Mental State.She couldn't draw intersecting pentagons or write a sentence.She showed severe right upper limb apraxia, and couldn't even hold the pen in her hand.While examining her, the right lower extremity showed Alien-limb phenomenon, and right upper extremity showed occasional myoclonus.She showed evidence of Balint's syndrome while reaching for Poster Presentations at the 8 th
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.380 | 0.055 |
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".