A Patient-Level Data Meta-analysis of the Abscopal Effect
Bibliographic record
Abstract
Purpose: The abscopal effect is defined when a form of local therapy causes tumor regression of both the target lesion and any untreated tumors. Herein cases of the abscopal effect were systematically reviewed and a patient-level data analysis was performed for clinical predictors of both duration of response and survival. Methods and Materials: The Population, Intervention, Control, Outcome, Study (PICOS) design approach, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) literature selection process, and Meta-analysis of Observational Studies in Epidemiology (MOOSE) were used to find articles published before September 2019 in MEDLINE/PubMed and Google Scholar. Inclusion criteria were (1) population: patients with reported abscopal response; (2) intervention: documented treatment(s); (3) control: none; (4) outcomes: overall and progression-free survival; and (5) setting: retrospective case reports. Time from treatment until abscopal response and time from abscopal response until progression/death were calculated. Univariate and multivariate analyses were conducted for survival outcomes. Results: Fifty studies (n = 55 patients) were included. Median age was 65 years (interquartile range [IQR], 58-70) and 62% were male. Fifty-four (98%) patients received radiation therapy, 34 (62%) received radiation therapy alone, 5 (9.1%) underwent surgery, 4 (7.3%) received chemotherapy, and 11 (20%) received immunotherapy. Median total dose was 32 Gy (IQR, 25.5-48 Gy) and median dose per fraction was 3 Gy (IQR, 2-7.2). Median time until abscopal response was 4 months (IQR, 1-5; min 0.5, max 24). At 5 years, overall survival was 63% and distant progression-free survival was 45%. No variables had statistical significance in predicting duration of response or survival. Conclusions: Almost all reported cases of the abscopal response are after radiation therapy; however, there are no known predictors of duration of response or survival in this population.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.056 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".