Are we missing PTSD in our patients with cancer? Part I.
Bibliographic record
Abstract
Posttraumatic Stress Disorder (PTSD) can be defined by the inability to recover from a traumatic event. A common misconception is that PTSD can only develop in circumstances of war or acute physical trauma. However, the diagnostic criteria of PTSD were adjusted in the Diagnostic Statistical Manual of Mental Disorders Fourth Edition (DSM-IV) to include the diagnosis and treatment of a life-threatening illness, such as cancer, as a traumatic stressor that can result in PTSD. The word 'cancer' is so strongly linked to fear, stigma, and mortality, that some patients are fearful to even say 'the C word'. Therefore, it is not surprising that patients may experience a diagnosis of cancer as sudden, catastrophic, and/or life-threatening. Cancer-related PTSD (CR-PTSD) can negatively affect a patient's psychosocial and physical well-being during treatment and into survivorship. Unfortunately, CR-PTSD often goes undiagnosed and, consequentially, untreated. This article provides a general overview of PTSD with cancer as the traumatic event in order to define CR-PTSD, and reviews the growing pool of literature on this topic, including prevalence, risk factors, characterization, and treatment of CR-PTSD. The purpose of this article is to spread awareness of this relatively newly defined and commonly missed disorder among patients with cancer to clinicians and patients alike.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".