The Canadian Journal of Diagnosis / December 2005 71 Assessing the Elderly: Getting into the Seven Habits
Bibliographic record
Abstract
Physicians and other health professionalsoften confront functional presentations and obscure historic details when dealing with the geriatric patients. Contrary to conventional thinking, the required diagnostic methods to make sense of such cases are available. In its essence, geriatric assessment consists of a proper history, physical exam and mental status exam, followed by a sober and thoughtful formulation. Despite patient complexity, geriatric assess-ment does not need to be a cumbersome process. The following seven habits of geriatric assessment will empower front-line clinicians, enhancing assessment accuracy and speed. Function as the language of illness in frail geriatrics Adults, of any age, usually manifest acute ill-ness with typical signs and symptoms. However, when they have other chronic physi-cal or cognitive conditions, the manifestation of acute illness may be atypical and reflect their underlying vulnerabilities. A short time course of illness suggests an acute process. The lan-guage of illness is the functional nature of the
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".