Measuring Retrograde Autobiographical Amnesia Following Electroconvulsive Therapy
Bibliographic record
Abstract
Retrograde amnesia following electroconvulsive therapy (ECT) is a major concern for both patients and clinicians. In contemporary ECT research, retrograde autobiographical amnesia (RAA) is commonly measured with instruments assessing autobiographical memory (AM) consistency over time. However, normal AM recall loses in consistency with the passage of time, and time has a differential effect on stability of personal memories. In addition, experiencing depression is associated with a decreased ability to recall specific AMs, and this difficulty may persist in the euthymic phase of recurrent depression. Despite these scientific facts, relatively few attempts have been made to accurately measure the specific effect of ECT on AM independent of both normal and mood-associated forgetting over time. This major gap in our knowledge prevents us at present from objectively quantifying the nature and extent of RAA associated with ECT. In turn, this hinders our identifying and implementing strategies for prevention or remediation of AM deficits. The present article aims to provide an up-to-date review and historical perspective of this major methodological conundrum for ECT research, highlight current issues in retrograde amnesia assessment following ECT, and propose directions for future studies. In conclusion, we suggest methods to reliably and specifically measure the extent and progression over time of ECT-associated RAA independently from persistent depressive symptoms' contribution and normal loss in AM consistency over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".