Bibliographic record
Abstract
The Economic Burden of PTSD. A brief review of salient literature Studies examine the economic burden of disease can be used to help policy makers set priorities for healthcare research and service provision [1]. These study types seek to quantify the economic impact of disease regardless of its origin or presentation. As such, policy makers are afforded the information allowing them to make decisions across and within therapeutic fields. Health economics deals with the scarcity of resources. Having accurate information about the economic cost of an illness helps policy makers prioritize, eventually leading to the allocation of healthcare resources [2]. Classified as an anxiety disorder, Post- Traumatic Stress Disorder (PTSD) is a condition that can have a significant negative impact on a person’s life [3]. The symptoms of PTSD include flashbacks, intrusive thoughts and nightmares, rumination and avoidance of areas or circumstances (WHO 2017). Figure 1 shows the ICD-10 (WHO 1992) diagnostic criteria for PTSD. Psychological trauma is associated with a number of mental health issues including schizophrenia [4], eating disorders [5] and addictions [6]. In recent years a link between PTSD and physical illnesses such as Type II diabetes [7], cardio-vascular disease [8], certain cancers [9] and fibromyalgia [10] has been noted in the literature. General population studies estimated a prevalence rate of PTSD of 3.6% (WHO 2013). The lifetime prevalence of PTSD in Vietnam War veterans was 16.9% [11] and a study of Canadian service veterans of the Iran war showed a prevalence of 12.9% [12]. UK service personnel returning from Iraq and Afghanistan report PTSD in 4% of cases [13]. In counties that have experienced civil conflict, the rates of PTSD are reported as 8.8% [14]. Given the wide-ranging impact then of PTSD, it is appropriate to examine the economic impact that PTSD has.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".