Malingering by Proxy: A Literature Review and Current Perspectives
Bibliographic record
Abstract
Malingering by proxy (MAL-BP) is a form of maltreatment that involves a caregiver who fabricates or induces signs or symptoms in a child, dependent adult, or pet in pursuit of external, tangible incentives. Rarely studied, MAL-BP has an unknown prevalence, and is a challenging diagnosis for healthcare professionals. Therefore, a comprehensive computer literature search and review was conducted. The review uncovered a total of sixteen case reports of MAL-BP (eleven human, five veterinary). The motive for malingering was financial in all human cases and medication-seeking in all veterinary cases. Although the strategies employed differed among the identified cases, common themes regarding the best approach to identification of MAL-BP cases became evident. A comprehensive workup including a thorough history, physical examination, appropriate neuropsychological testing, and relevant collateral information forms the basis of an effective identification strategy. The optimal method of management is currently unclear due to a relative paucity of data and guidelines. However, management of these cases would likely include a team-based approach with a prudent assessment of safety for the proxy and a low threshold for referral to appropriate services. Long-term follow-up is essential and should be approached from a biopsychosocial perspective. Attention, research, and guidance on this topic are needed to develop further evidence-based guidelines for the identification and management of MAL-BP.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".