We break down how veterans win claims for secondary conditions and aggravation by using strong medical opinions, baselines, and clear clinical logic. A real case shows how a congenital heart valve, ignored in service, becomes service connected years later with the right evidence.

• What a medical opinion or IMO is and why it matters
• How pes planus can lead to ankle arthritis
• Why sleep apnea often wins as a secondary claim
• Obesity as an intermediate step linking pain to apnea
• Stacking risk factors instead of relying on one cause
• How to service connect injuries from falls
• Aggravation logic, baselines, and rating the increase
• Cognitive impairment claims tied to PTSD and self-medication
• AMA-era pitfalls, forms, and common denial errors
• Using databases to explore viable secondary pathways

If you need an IMO, call 888-448-1011 or visit valor4vet.com

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We break down how to use independent medical opinions to win VA disability claims, from when to get one to what evidence changes outcomes. Two detailed case studies show how a clear timeline and the right medical logic can overcome templated denials.

• Why qualification and VA training give private opinions weight
• When to file a nexus letter and when to skip it
• Fully developed claims versus standard claims
• Supplemental claims versus higher-level review
• What records to gather before you file
• How to get doctors to state causal links in notes
• Obesity as an intermediate step for secondary conditions
• Tying mono and EBV to later cancers and fatigue
• Preempting templated denial language with precise rationale

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We test where AI can actually help veterans build stronger Nexus letters and where it fails hard, from fake citations to the wrong legal phrasing. Bethanie Spangenberg shares practical prompts, research tactics, and quality standards that keep letters credible and readable for VA raters.

• defining what a strong Nexus letter must include
• “at least as likely as not” vs malpractice language
• writing for raters with clear, low-jargon explanations
• how AI helps: summaries, translation, structure, prompts
• where AI fails: hallucinated sources, generic templates
• verifying research, citations, and URLs before use
• handling obesity and other medical risk factors
• statements to fill long gaps in treatment history
• research hierarchy: systematic reviews to cohort studies
• privacy cautions when using public AI tools
• internal workflows, grammar tools, and quality control
• actionable prompts to find relevant medical literature

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