Autobiographical memory predicts postsurgical pain up to 12 months after major surgery
Bibliographic record
Abstract
ABSTRACT: Recent cross-sectional studies have identified differences in autobiographical memory (AM) among individuals with chronic pain, but the temporal relationship between the 2 is unknown. Moreover, AM has yet to be studied in patients undergoing major surgery. This study addressed these gaps by conducting a prospective, longitudinal study of memory performance, postsurgical pain, and psychosocial factors in 97 adult participants scheduled for major surgery. Memories were evaluated using the Autobiographical Memory Test before and one month after surgery when participants were asked to recall personal events related to positive and pain-related word cues. Responses were coded for level of specificity, emotional valence, and surgery-related content. Questionnaires assessing presence/absence of pain and psychological functioning were administered before and at 1-, 3-, 6-, and 12-month follow-ups. Generalized estimating equations modelled pain at each postsurgical time point with memory variables as predictors. As hypothesized, higher numbers of specific pain memories recalled before surgery predicted lower odds of pain across all time points (OR = 0.58, 95% CI [0.37-0.91]). Participants who took longer to recall pain memories before surgery (OR = 2.65, 95% CI [1.31-5.37]) and those who produced more surgery-related content at the one-month assessment (OR = 1.31, 95% CI [1.02-1.68]) had greater odds of reporting postsurgical pain up to 12 months later. These findings indicate that presurgical AM biases are risk factors for development and maintenance of postsurgical pain. To the extent that these biases are causal, presurgical interventions that modify the quality and content of patients' memories may prove to be promising strategies in the prevention of chronic postsurgical pain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".