Is it possible to join a national multi-center clinical trial during cyberattack?
Bibliographic record
Abstract
Background: In October 2023, our hospital encountered a major cyberattack resulting in temporary loss of institutional electronic medical record (EMR) and networks. However, we opened PATRON trial (ClinicalTrials.gov NCT # 04557501) to determine if PSMA PET/CT guided intensification of therapy is superior to standard of care (SOC) for high-risk prostate cancer. Hypotheses: It is possible to join a national multi-center phase III trial during cyberattack. Methods: An interprofessional team worked closely to screen patients and obtain informed consents. Patients randomized into the PET arm were subsequently referred to the closest cancer facility to have PSMA-PET scan (2-hour drive each way). All clinical data were documented in paper-based format and entered into EMR after the hospital recovered from the cyberattack. Results: During 3 months, 21 patients were screened, 10 eligible for the PATRON trial, and 5 signed consents. Two were randomized to have PET scans. One had no metastasis on PET and received SOC, along with 3 patients in control arm. The other patient had PET finding of oligometastases in one single lymph node and received same SOC plus 14Gy boost. As a comparison, a total of 794 patients in Canada were recruited from 19 institutions during 3.5 years. Our institution's quarterly recruiting rate was much higher than the national average (5 vs 3.2). Conclusions: Cyberattacks are potentially catastrophic especially in cancer patients enrolled in clinical trials. Our institutional experience suggested that collaboration with another cancer center nearby could successfully enroll eligible patients into a large multi-center phase III trial.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".