The Effect of Integrative Naturopathic Oncology Including Modulated Electrohyperthermia on Survival Outcome among Glioblastoma Multiforme Patients: A Retrospective Study
Bibliographic record
Abstract
Background: Glioblastoma multiforme (GBM) is an aggressive brain tumor with limited treatment options and poor prognosis. Emerging evidence suggests that integrative oncology approaches may provide survival benefits when combined with conventional treatments. This study examines whether an integrative oncology treatment plan incorporating modulated electro-hyperthermia (mEHT) improves survival in GBM patients. Methods: This retrospective cohort study analyzed data from GBM patients treated at the Integrated Health Clinic (IHC) between 2010 and 2024. Survival outcomes were compared between IHC patients receiving adjuvant integrative naturopathic therapies and a matched control group from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database. Kaplan–Meier survival estimates, and Cox proportional hazard models were conducted to assess survival differences. Secondary analyses evaluated the impact of treatment timing (≤120 days vs >120 days post-diagnosis) and age on survival. Results: The integrative treatment cohort demonstrated a lower hazard of mortality than the SEER group (HR = .72, 95% CI: .53-1.00, P -value = .05). The treatment benefit was greater among IHC patients who started treatment within 120 days of diagnosis (HR = .52, 95% CI: .33-.83, P -value = .006) and those under age 50 (HR = .51, 95% CI: .31-.85, P -value = .009). Conclusions: The findings suggest that an integrative naturopathic approach incorporating mEHT may improve survival outcomes in GBM patients. Patients initiating integrative treatment earlier experienced a greater survival benefit, as did patients under 50 years of age. Further studies, ideally prospective randomized controlled trials, are warranted to validate these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".