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Record W2129341143 · doi:10.1111/1556-4029.12977

Malingering by Proxy: A Literature Review and Current Perspectives

2015· review· en· W2129341143 on OpenAlexaff
Adam Amlani, Gurinder S. Grewal, Marc D. Feldman

Bibliographic record

VenueJournal of Forensic Sciences · 2015
Typereview
Languageen
FieldMedicine
TopicChild Abuse and Related Trauma
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMalingeringMedicineProxy (statistics)ReferralIncentiveBiopsychosocial modelPsychiatryPsychologyClinical psychologyFamily medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.385
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations20
Published2015
Admission routes1
Has abstractyes

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