To RCT or Not to RCT: How to Change Practice for Rare Cancers?
Bibliographic record
Abstract
In the world of evidence-based medicine, a dogmatic hierarchy of clinical trial design 1 has conditioned us to only change practice based on randomized data and to discount observational studies. Thus, defining treatment paradigms for rare cancers (or rare subsets of cancers) remains a challenge. If a randomized controlled trial (RCT) cannot be completed, how does one establish practice-changing results? In the article that accompanies this editorial, Tao et al 2 report a retrospective analysis of 79 patients and the impact of radiation dose on outcomes for inoperable, locally advanced intrahepatic cholangiocarcinoma (IHC).Tantalizingly,patientstreatedto higher than the median radiation dose (biologic equivalent dose 80.5 Gy, equivalent to approximately 58 Gy in 15 fractions) achieved remarkable levels of long-term local control, overall survival, and progression-free survival (78%, 58%, and 39%, respectively, at 3 years) with a surprisingly low incidenceoftoxicities.Ofnote,biologicequivalentdosewastheonly variable statistically associated with outcomes on multivariable analysis. The outcomes of these patients are within reported outcomes of patients with ICH in surgical series. In contrast, the expected outcome fromstandard of caretreatmentfor these patients can be estimated from the results of the randomized ABC-02 trial, 2 which treated a mix of inoperable patients (approximately 25% locally advanced and 75% metastatic) with systemic therapy alone, 3 achieving a 3-year survival rate that was close to zero.
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 | 0.557 | 0.817 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.025 | 0.043 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.041 | 0.048 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".