Approach to hoarding in family medicine: beyond reality television.
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.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".