Gender Dysphoria and Dissociative Identity Disorder: A Case Report and Review of Literature
Bibliographic record
Abstract
BACKGROUND: World Professional Association for Transgender Health guidelines support the importance of a mental health assessment before providing medical treatment for Gender Dysphoria (GD). During this assessment, patients without GD but with mental health disorder and who request treatment for GD should be excluded. Dissociative Identity Disorder (DID) is a poorly known mental disorder which can be confused for GD. AIM: To provide a case report of a patient suffering for DID but asking for treatment for GD and to provide a review of the literature on GD and DID. METHOD: A case report assessment and follow-up was described and a systematic review of the literature was performed in Pubmed, PsychInfo, and Embase databases. OUTCOME: To provide all cases with assessment and follow-up of DID and GD. RESULTS: The case report describes a man suffering from DID and asking for hormonal treatment for GD. After assessment the patient was able to let go of his wish for treatment for GD and begin psychotherapy for DID. During the literature review 11 articles were included. 3 articles showed a prevalence of DID of 0%, 0.8% and 1,5% in GD samples. 5 articles were case reports of patients with both diagnoses of GD and DID which showed the complexity of the care of these patients. 2 articles were case reports, where a GD diagnosis was done first, and then counseling for GD was proposed. After a second session, the diagnosis was changed for DID. In 1 other case report and our case report there was a description of 2 persons suffering from DID and asking for treatment for GD. CLINICAL IMPLICATIONS: Our review shows the complexity of providing care to patients with a comorbidity of GD and DID, as well as the complexity of making the differential diagnosis between GD and DID. STRENGTHS AND LIMITATIONS: A systematic review was performed on these rare cases. Our study presents the results for a small group of patients. CONCLUSIONS: This article provides the first systematic review on GD and DID and shows that DID in a GD sample does not seem to be higher than in the general population. In addition, it allow clinicians to gain better knowledge about patients suffering from both DID and GD and patients suffering from DID who ask for GD treatment. Soldati L, Hasler R, Recordon N, et al. Gender Dysphoria and Dissociative Identity Disorder: A Case Report and Review of Literature. Sex Med 2022;10:100553.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".