Cognitive-Behavioral Treatment of Tomophobia (Fear of Medical Procedures) Using an Innovative, Virtual-Reality-Augmented Approach: A Case Study in a Patient With Breast Cancer
Bibliographic record
Abstract
Most patients experience elevated anxiety prior to surgery; however, a subset of these individuals will present with clinically significant preoperative anxiety and meet criteria for tomophobia. Tomophobia is a subtype of Specific Phobia characterized by an intense fear of medical procedures, which can lead to avoidance of necessary, even lifesaving, interventions. Despite its clinical significance, research on tomophobia remains limited, and best-practice interventions are not well established. This case study illustrates a promising Cognitive Behavioral Therapy (CBT) approach that incorporates interdisciplinary care and innovative exposure methods using virtual reality (VR). The patient was a treatment-naïve middle-aged woman who was refusing necessary surgical care for breast cancer due to a fear of surgery (i.e., Specific Phobia, Blood-Injection-Injury Type). Assessment and treatment were delivered over 12 preoperative sessions with one postoperative follow-up session. Engagement in treatment resulted in functional improvements, including willingness to undergo surgery, and clinically significant reductions in the validated Severity Measure for Specific Phobia (intake score = 25; final preoperative session score = 7; postoperative session score = 5). This case study highlights how interdisciplinary care and VR can be integrated to systematically expose patients to typically inaccessible yet triggering environments, such as the operating room, providing useful guidance for clinicians treating tomophobia and significant preoperative anxiety.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".