Identifying Poor Studies · Cancer Warriors Vault

Identifying Poor Studies

Jason raised a vital concern: how do we critically evaluate the quality of scientific studies? Many studies may be poorly designed, biased, or irrelevant to our specific cancer type. The group emphasized the need for a shared education on interpreting research, highlighting how confirmation bias, funding sources, and study design flaws can mislead even well-meaning patients. Jason emphasized that trusting a study simply because we “like” the conclusion is dangerous.

✅ Supplements & Compounds Discussed (Tangential Mentions)

● Cimetidine (referenced via separate post)

● Fenbendazole & Mebendazole (referenced by David Fajardo in relation to Stanford study)

✅ Therapies & Strategies

● Evidence Literacy: Jason proposes learning how to evaluate:

○ Research questions

○ Study design (RCTs vs. observational)

○ Sample size & population

○ Controls & endpoints

○ Biases (funding, publication)

○ Statistical significance vs. clinical relevance

● Community Data Collection: Melissa Mantilla suggests creating a shared framework to collect and review patient-reported outcomes from KMT protocols.

✅ Lab Testing & Data Interpretation Themes

● Concerns over SOC ignoring relevant data: David Fajardo questioned why a Stanford-published mebendazole study isn’t reflected in his Stanford oncologist’s care suggestions.

● Gaps in translation from research to clinical practice: Melissa expressed frustration that promising low-toxicity treatments get buried in literature without progressing to standard care — even after 15+ years.

✅ Resources & References Shared

● Cimetidine for colon cancer thread

● YouTube: Dr. John Campbell – FenBen for cancer?

● Skool thread on clinical study literacy (by Jason)

● Tools in development:

○ Simon Lown is working on an AI-powered study evaluator that will assign a metabolic therapy relevance score and redirect users to a summarized review of each study.

✅ Key Takeaways

● Not all studies are created equal — Jason calls for our community to stop blindly accepting or rejecting studies and instead develop the skills to analyze them.

● Big pharma bias is real, but not absolute — Some industry studies are valid. Dismissing them entirely may cause us to miss useful data.

● Study relevance varies by cancer type — a GBM study may not apply to colon cancer.

● We need a community-level framework to capture real-time patient results — especially with off-label and metabolic therapies.

● Prayer, compassion, and collaboration remain central — members offered encouragement, practical tips, and personal stories of facing similar frustrations.

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