Posttraumatic Stress Disorder: The Misappropriation of Military Suicide Causation and Medication Treatment of Posttraumatic Stress Disorder
Bibliographic record
Abstract
Dear Editor:I read with interest your September 2014 issue of The CJP. Brunet and Monson1 and Sareen2 deserve some compliment for shading light on suicides in the military and the treatment of posttraumatic stress disorder (PTSD).PTSD is but one of the causes of suicides here but it carries far more political and media gravitas. The need to pay attention to psychological autopsies of individual suicides is a more rational approach and the golden road to solving the problem of suicides in the military.I write as someone who was on the front line treating veterans and soldiers during a period of 3 years in a remote Canadian Armed Forces (CAF) base, a time frame encompassing the war in Afghanistan and the Haiti earthquake disaster, but also treated veterans of the Rwandan genocide, the Oka riots in Quebec, and the Bosnian crisis. At this CAF base, for this time period, no Canadian soldier or veteran died by suicide.In the Medical Professional Technical Suicide Review Report of the National Defence published in September 2013,3 following a spate of soldier suicides, the authors identified the following reasons, with percentages, among completed suicides as the major causes: relationship failure and conflict (44.4%), financial problems (15.8%), chronic physical health problems (13.2%), legal disciplinary issues (10.5%), and mental health history that collectively accounted for 47.4%, of which, 21% suffered from depression, 18.5% suffered from PTSD, and 21.1% suffered from substance use disorder. In an original investigation of risk factors for suicides among American service men, LeardMann et al4, p 496 identified male sex, depression, manic depressive disorder, heavy or binge drinking, and alcoholrelated problems as factors. They failed to identify any deployment-related factors as risk factors, including PTSD.Effective management of suicidal and homicidal behaviours must be achieved in 2 sequential stages of assessment aiming to place the patient in 1 of 2 suicide risk groups: immediate suicide or a suicide flashpoint group where the psychache as enunciated by Edwin Shneidman5 is so overwhelming that suicide is the only act that can remove the pain; or chronic suicide risk group, which is the universal suicide risk of 1% plus the risk attached to individual psychiatric diagnosis. Most of the rash of suicides that occurred in the last couple of years, and particularly earlier this year, were the results of failures in the immediate or flashpoint group.From my personal experience, suicide flashpoint group was mostly comprised of relationship failures, conflict with chain of command, acute financial distress, angst against Veterans Affairs or disability and pension agencies over benefits and uncertainty over pension eligibility, intoxication, very severe depression with suicidal intention, panic or anxiety attacks, and so on. PTSD, per se, will not feature here except in the company of the above factors or where insomnia and nightmares have been so severe and poorly treated.Insomnia and nightmares are sentinel symptoms of PTSD. Failure to tackle these early and decisively will doom the patient to the extent that all the other PTSD symptoms will become worse.6Prazosin is the most effective medication management of nightmares today. It is not promoted as it should because it is long out of patent, dirt cheap, and not backed by big pharma.Daniel Chinedu Okoro, MD, DPM, LMCC,FRCPC, DABPN, FAPACalgary, AlbertaReferences1. Brunet A, Monson E. Suicide risk among active and retired Canadian soldiers: the role of posttraumatic stress disorder. Can J Psychiatry. 2014;59(9):457-459.2. Sareen J. Posttraumatic stress disorder in adults: impact, comorbidity, risk factors, and treatment. Can J Psychiatry. 2014;59(9):460-467.3. Collins R, Matheson H, Sedge P, et al. Medical professional technical suicide review report. Surgeon General Health Research Report. …
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".