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Pharmacological treatment of combatinduced PTSD: a literature review

2010· review· en· W93305844 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBritish Journal of Nursing · 2010
Typereview
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMedicineMEDLINEPsychotherapistPsychologyPsychiatryIntensive care medicinePolitical science

Abstract

fetched live from OpenAlex

Historically, soldiers have returned from war changed men. Over the years there has been an increase in awareness of post-traumatic stress disorder (PTSD) and the impact of diagnosis. Treatment of PTSD presents a challenge on every level. This literature review provides some insight into the risks and benefits of three groups of drugs commonly prescribed for combat-induced PTSD: beta-blockers, selective serotonin reuptake inhibitors (SSRIs) and benzodiazepines (BZDs). When prescribed in conjunction with other non-pharmacological treatments, these drugs help to minimize, and in some cases eliminate, the signs and symptoms of PTSD. Combination therapy would ideally result in better compliance and eventual completion of treatment programmes provided for PTSD sufferers. Healthcare professionals strive to provide patients with holistic care. Patients present with unique mental and physical intricacies, and nurses and health professionals must peel away the layers to uncover the nature of the PTSD. While there are many aspects to PTSD treatment, this literature review focuses on pharmacological treatment, specifically beta-blockers, SSRIs and BZDs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.258
GPT teacher head0.540
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it