Abstract A13: Linking cancer and vaccine registries in a retrospective cohort study: Risk of metastasis following perioperative vaccine use among cancer patients
Bibliographic record
Abstract
Abstract Background: Surgical removal of solid tumors is a frequent treatment for cancer patients. Immunosuppression after surgery, which suppresses natural killer (NK) cells, increases the risk of metastasis (mets). Preclinical and animal studies have shown that perioperative vaccination can prevent postoperative NK cell dysfunction and may reduce the risk of mets. In the first epidemiologic study of its kind, we investigated this intriguing hypothesis in the Canadian province of Manitoba (MB). Methods: We linked the MB Cancer Registry, containing all cancer diagnoses and cancer-specific therapy in MB, with the MB Immunization Monitoring System and other administrative health databases covering >99% of the population. We identified all first cancer surgeries conducted during 1994-2014 in MB for an invasive, nonmetastatic, primary, solid cancer. We followed patients from their date of surgery to the earliest date of metastasis, 2nd primary cancer, 2nd cancer surgery, death, loss of coverage, or study end. We used competing-risk Cox models adjusted for age at surgery, gender, comorbidities, and cancer stage at diagnosis. Results: Out of 29,120 persons with a cancer surgery, 0.2% and 0.4% received a vaccine, mostly seasonal flu vaccines, 2-4 and 0-5 days before surgery respectively, and 21.8% developed metastasis during follow-up. Compared to those vaccinated 30 days before to 14 days after surgery outside of the specific time window, the hazard ratio of the association between vaccination 0-5 days before surgery and metastasis was 0.72 (0.32-1.61) for a 15-year follow-up, and the association was 0.44 (0.11-1.78) for vaccination 2-4 days before surgery. Conclusion: In this relatively small study, we found evidence for a strong association between vaccination just before cancer surgery and long-term occurrence of mets. Further studies of this associations in humans are warranted; they must be sufficiently large to investigate potentially narrow windows of vaccine effectiveness, and they could use biomarkers of mets as intermediate endpoints. Citation Format: Christiaan H. Righolt, Geng Zhang, Salaheddin M. Mahmud. Linking cancer and vaccine registries in a retrospective cohort study: Risk of metastasis following perioperative vaccine use among cancer patients [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A13.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".