A critical review of methodological quality in functional neuroimaging studies on dissociative identity disorder
Bibliographic record
Abstract
Background: Dissociative identity disorder remains contested. The debate hinges on whether memories carry over between identity states and whether those states are truly distinct, but most evidence rests on self report rather than direct memory tests. Neuroimaging has been advanced as an indirect, non self-report approach by scanning individuals with DID in different identity states and comparing them with simulators or other groups. Objective: To evaluate how studies that scan people with DID in more than one identity state inform the core memory claims of DID, by assessing their methodological quality. Methods: Systematically reviewing studies from the past 40 years, quality was assessed using GRADE criteria and the Newcastle-Ottawa scale. Results: Of the nine studies reviewed, many lacked specific aims and only one stated clear hypotheses throughout. The results further indicated several concerns related to diagnostic comorbidity, and absence of clinical comparisons, reverse inference, and post hoc reasoning. Conclusions: On current evidence, functional imaging across identity states does not support firm claims about identity fragmentation or inter identity amnesia, nor does it decide between trauma based and sociocognitive accounts. Methodological refinement and direct tests of memory transfer are needed for progress.
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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 | 0.060 | 0.183 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".