When good news are not enough: Predicting trauma-related symptoms and non-specific distress after negative and positive breast biopsy results
Bibliographic record
Abstract
The disclosure of breast biopsy results, whether indicating cancer (positive) or not (negative), can be experienced as a psychologically distressing event and could involve perceived threat to life. As peritraumatic distress is a predictor of post-event psychological symptoms, its investigation in the context of breast cancer screening could improve early identification of individuals at risk for persistent distress. This study first examined the proportion of individuals exceeding the clinical threshold for post-traumatic stress symptoms (PTSS) and non-specific distress (DT) at 7 days and 1-month post-biopsy results. It then tested whether peritraumatic distress experienced at the time of the result disclosure predicted PTSS and DT at both timepoints. An exploratory objective assessed whether perceived life threat at disclosure predicted distress outcomes differently based on biopsy results. In a sample of 191 participants, 85.9% exceeded the PTSS threshold at 7 days and 73.6% at 1 month. In contrast, 12.3% exceeded the DT threshold at 7 days, and 8.5% at 1 month. Peritraumatic distress significantly predicted PTSS at 7 days ( B = 0.44, SE = 0.16, t = 2.80, p = .006) and 1 month ( B = 0.76, SE = 0.18, t = 4.26, p < .001), and DT at only 7 days ( B = 0.08, SE = 0.03, t = 2.60, p = .010), regardless of diagnosis outcome. Exploratory analyses showed that perceived life threat at disclosure predicted PTSS at both timepoints, only among individuals with negative results ( B = 6.59, SE = 2.03, 95% CI [2.58, 10.59], p < .001). These findings highlight that the screening process itself can be perceived as life-threatening, and that assessing peritraumatic distress at the time of biopsy results may help prevent lasting symptoms, even without a cancer diagnosis.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".