The primary care physician and Alzheimer's disease: An international position paper
Bibliographic record
Abstract
This paper aims to define the role of the primary care physician (PCP) in the management of Alzheimer's disease (AD) and to propose a model for a work plan. The proposals in this position paper stem from a collaborative work of experts involved in the care of AD patients. It combines evidence from a literature review and expert's opinions who met in Paris, France, on July 2009 during the International Association of Geriatrics and Gerontology (IAGG) World Congress. The PCP's intervention appears essential at many levels: detection of the onset of dementia, diagnostic management, treatment and follow-up. The key role of the PCP in the management of AD, as care providers and care planners, is consolidated by the family caregiver's confidence in their skills. In primary care practice the first step is to identify dementia. The group proposes a "case finding" strategy, in target situations in which dementia should be detected to allow, secondarily, a diagnosis of AD, in certain cases. We propose that the PCP identifies 'typical' cases. In typical cases, among older subjects, the diagnosis of "probable AD" can be done by the PCP and then confirm by the specialist. While under-diagnosis of AD exists, so does under-disclosure. Disclosure to patient and family should be done by both specialist and PCP. Then, the PCP has a central role in management of the disease with the general objectives to detect, prevent and treat, when possible, the complications of the disease (falls, malnutrition, behavioural and psychological symptoms of dementia). The PCP needs to give basic information to the caregiver on respite care and home support services in order to prevent crisis situations such as unplanned institutionalisation and "emergency" hospital admission. Finally, therapeutic research must be integrated in the daily practice of PCP. It is a matter of patients' right to benefit from access to innovation and clinical research whatever his age or diseases, while of course fully respecting the rules and protective measures that are in force.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".