Giving a leg up part 2: the ethical challenges of Body Integrity Dysphoria
Bibliographic record
Abstract
Introduction Body Integrity Dysphoria (BID) is a diagnosis, newly described in ICD-11, “characterised by an intense and persistent desire to become physically disabled in a significant way… accompanied by persistent discomfort, or intense feelings of inappropriateness concerning current non-disabled body configuration”. Patients with BID may request the amputation of healthy limbs but this raises multiple ethical challenges. Objectives By the end of the presentation, participants 1) will better understand the new diagnosis of Body Integrity Dysphoria; 2) will be able to have some landmarks to evaluate and manage this rare condition 3) will discern the ethical challenges raised by an elective or emergent amputation request. Methods We present a complex case we faced in Quebec City, Canada. A young adult admitted to the intensive care and burn unit was referred to our Consultation Liaison (CL) team. For the second time in a year, the individual deliberately burned his leg, with the intention of having an amputation. Based on the available literature and our experience, we explore the ethical aspects of this case. Results For this situation, the multidisciplinary team faced uncertainty and ambivalence toward the best treatment options. Deontological concerns and ethical issues emerged from the patient’s request for amputation. Conclusions We outline how ethical concepts helped us to gain a shared comprehension of the patient’s extraordinary request, both during treatment and afterwards. Disclosure No significant relationships.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".