G.K. Wilcock, R.S. Bucks, and K. Rockwood (Eds.). Diagnosis and Managementof Dementia: A Manual for Memory Disorders Teams. New York: Oxford University Press, 1999.
Bibliographic record
Abstract
RÉSUMÉ Ce manuel vise à favoriser la constitution d'équipes s'occupant de troubles de la mémoire, au sein de la communauté ou en établissements. Les auteurs présentent les résultats d'une étude de 28 cliniques du monde illustrant les diverses constitutions et les différents modes de fonctionnement des équipes. Chaque équipe vise à diagnostiquer les troubles de la mémoire et quelques-unes s'occupent aussi de traitement, de gestion et de recherche. La manuel présente des forces et des faiblesses de sorte que certains chapitres sont plus ou moins utiles; cependant, les besoins propres à chacun et l'expertise personnelle détermineront la valeur relative du texte pour, par exemple, les psychologues par opposition aux gériatres. On estime que le document se rapproche plus du guide que du documentaire. On ne peut mettre en doute l'utilisé d'un tel livre et il constitue une excellente base à la constitution des données sur les cliniques des troubles de la mémoire.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.019 |
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".