Approach to hoarding in family medicine: beyond reality television.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To review the presentation of hoarding and provide basic management approaches and resources for family physicians. SOURCES OF INFORMATION: PubMed was searched from 2001 to May 2011. The MeSH term hoarding was used to identify research and review articles related to the neuropsychological aspects of hoarding and its diagnosis and treatment. MAIN MESSAGE: Hoarding is often a hidden issue in family medicine. Patients with hoarding problems often present with a sentinel event such as a fall or residential fire. Although hoarding is traditionally associated with obsessive-compulsive disorder, patients more commonly have secondary organic disease associated with hoarding behaviour or have hoarding in absence of substantial compulsive traits. Hoarding disorder is expected to be included in the Diagnostic and Statistical Manual of Mental Disorders, 5th edition. Management is best provided by a multidisciplinary approach when possible, and an increasing number of centres provide programs to improve symptoms or to reduce harm. Pharmacologic management has been shown to be of some help for treating secondary causes. In the elderly, conditions such as dementia, depression, and substance abuse are commonly associated with hoarding behaviour. Attempts should be made to keep patients in their homes whenever possible, but an assessment of capacity should guide the approach taken. CONCLUSION: Hoarding is more common than family physicians realize. If hoarding is identified, local resources should be sought to assist in management. Assessment and treatment of underlying causes should be initiated when secondary causes are found. It is expected that primary hoarding will be a new diagnosis in the Diagnostic and Statistical Manual of Mental Disorders, 5th edition.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it