A Systematic Review and Meta-Analysis on the Survival of Cancer Patients Treated with a Fermented <b><i>Viscum album</i></b> L. Extract (Iscador): An Update of Findings
Bibliographic record
Abstract
PURPOSE: We aimed at updating the evidence found in controlled studies addressing general and event-free survival of cancer patients treated with the fermented mistletoe extract Iscador. METHODS: The databases Embase, PubMed, CAMbase, Scopus, AMED and Cochrane were searched for clinical studies on cancer patients treated with Iscador. Quality of studies and risk of bias were evaluated according to the Cochrane guidelines and the Newcastle Ottawa Scale. Outcome data were expressed as hazard ratios (HR) and the respective 95% confidence intervals (CI). Meta-analysis was carried out using a random-effects model. RESULTS: Eighty-two controlled studies met the inclusion criteria, of which 32 with 55 strata provided data for extracting HR and CI. The overall HR was 0.59 (95% CI: [0.53; 0.65], p < 0.0001) in favour of Iscador treatment. Heterogeneity of study results was moderate (I2 = 50.9%; p < 0.0001, τ2 = 0.053). Meta-regression did not reveal significant effects of sample size or study design. However, significant differences were found between cancer entities (p < 0.01), with most pronounced effects in cervical (HR = 0.43) and less pronounced effects in lung cancer (HR = 0.84). CONCLUSIONS: We found almost identical effects on cancer survival based on a broader database of higher quality. However, none of the studies was blinded and, therefore, there might be risk of performance bias. Implications for cancer survivors are as follows: findings indicate that adjuvant treatment of cancer patients with Iscador can be associated with a better survival.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".