Time management: Improving the timing of post-prostatectomy radiotherapy, clinical trials, and knowledge translation
Bibliographic record
Abstract
BACKGROUND: Management of prostate cancer after surgery is controversial. Past studies on adjuvant radiotherapy (aRT) for higher-risk features have had conflicting results. Through the collaborative conversations of the global radiation oncology Twitter-based journal club (#RadOnc #JC), we explored this complex topic to share recent advances, better understand what the global radiation oncology community felt was important and inspire next steps. METHODS: We selected the recent publication of a landmark international randomized controlled trial (RCT) comparing immediate and salvage radiotherapy for prostate cancer, RADICALS-RT, for discussion over the weekend of January 16 to 17, 2021. Coordination included open access to the article and an asynchronous portion to decrease barriers to participation, cooperation of study authors (CP, MS) who participated to share deeper insights including a live hour, and curation of related resources and tweet content through a blog post and Wakelet journal club summary. DISCUSSION OF RESULTS: Our conversations created 2,370,104 impressions over 599 tweets with 51 participants spanning 11 countries and 5 continents. A quarter of the participants were from the US (13/51) followed by 10% from the UK (5/51). Clinical or Radiation Oncologists comprised 59% of active participants (16/27) with 62% (18/29) reporting giving aRT within the last 5 years. Discussion was interdisciplinary with three urologists (11%), three trainees (11%), and two physiotherapists (7%). Four months after the journal club its article Altmetric score had increased by 7% (214 to 229). Thematic analysis of tweet content suggested participants wanted clarification on definitions of adjuvant (aRT) and salvage radiotherapy (sRT) including indications, timing, and decision-making tools including guidelines; more interdisciplinary and cross-sectoral collaboration including with patients for study design including survivorship and meaningful outcomes; more effective knowledge translation including faster clinical trials; and more data including mature results of current trials, particular high-risk features (Gleason Group 4+, pT4b+, and margin-positive disease), implications of newer technologies such as PSMA-PET and genomic classifiers, and better explanations for practice pattern variations including underutilization of radiotherapy. This was further explored in the context of relevant literature. CONCLUSION: Together, this global collaborative review on the postoperative management of prostate cancer suggested a stronger signal for the uptake of early salvage radiation treatment with careful PSA monitoring, more sensitive PSA triggers, and expected access to radiotherapy. Questions still remain on potential exceptions and barriers to use. These require better decision-making tools for all practice settings, consideration of newer technologies, more pragmatic trials, and better use of social media for knowledge translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".