A Real-World Analysis of Total Neoadjuvant and Other Therapies for Locally-Advanced Rectal Cancer
Bibliographic record
Abstract
In recent years, the treatment of locally-advanced rectal cancer (LARC) has been spotlighted in a number of landmark, practice-changing trials, heralding in the age of so-termed “total neoadjuvant therapy” (TNT). However, the lack of a common control arm, standard inclusion criteria, or, indeed, definition of TNT necessitates further study in this field, particularly using real-world data. We conducted a retrospective analysis of all patients treated for LARC at a single Canadian academic cancer centre between July 1st 2019 and July 14th 2022. This includes the June 2020 incorporation of TNT. 290 patients were included in the final analysis, and 124 variables were collected on each patient, including demographics, treatment type, adverse events, response to treatment, surgery versus watchful waiting and survival. Our findings demonstrate that both TNT and non-TNT regimens are highly toxic, with regards to both overall toxicity and grade 2+ adverse effects. 62 (96.9%) of patients on TNT experienced toxicities, compared to 186 (82.3%) of the non-TNT patients. Grade 2+ toxicities were 46 (71.9%) and 143 (63.3%), respectively. TNT, however, was associated with improved rates of complete response, compared to non-TNT treatment regimens (44% vs 24%). These findings suggest that TNT harbours both benefit and risk in a real-world population, and both of these aspects should be considered when deciding on a treatment approach for an individual patient. More work in the area is needed to determine longer term survival, as well as impact of surgery versus organ preservation approach on patient outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".