Skeletal Manifestations of Gender-Affirming Medical Interventions for Aiding in the Preliminary Identification of Trans Individuals
Bibliographic record
Abstract
Because of systemic discrimination, transgender individuals are at greater risk of being the victims of violence and of homicide. Accurate post-mortem identification from skeletonized remains of transgender individuals must be incorporated into a new standard for forensic anthropological analyses. A critical component of any investigation is the assessment of skeletal remains for evidence of gender-affirming care. A systematic review of the current medical literature was conducted to compile in one document descriptions of changes that could be used by forensic anthropologists to recognize skeletal manifestations resulting from gender-affirming surgeries, including facial feminization surgery (FFS), shoulder width reduction surgery, and limb-lengthening procedures. These skeletal changes, when present bilaterally and without evidence of healed trauma, serve as key indicators of a person’s transgender identity postmortem. Recognizing common patterns in bone structure alterations due to gender-affirming interventions will assist in identifying transgender individuals and providing closure for families. By integrating markers from gender-affirming care practices into forensic investigations, this research contributes to more inclusive and rigorous forensic investigations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".