Watch-and-Wait as a Therapeutic Strategy in Rectal Cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pathological complete response is seen in approximately one fifth of rectal cancer patients following neoadjuvant chemoradiation. Since these patients have excellent oncological outcomes, there has been a rapidly growing interest in organ preservation for those who develop a clinical complete response. We review the watch-and-wait strategy and focus on all aspects of this hot topic, including who should be considered for this approach, how should we identify treatment response and what are the expected outcomes. RECENT FINDINGS: The major challenges in interpreting the data on watch-and-wait are the significant heterogeneity of patients selected for this approach and of methods employed to identify them. The evidence available comes mostly from retrospective cohort studies, but has shown good oncological outcomes, including the rate of successful salvage surgery, locoregional control and overall survival. SUMMARY: There is currently not enough and not robust enough evidence to support watch-and-wait as a standard approach, outside a clinical trial, for patients achieving clinical complete response following neoadjuvant chemoradiation. Furthermore, there is a lack of data on long-term outcomes. However, the results we have so far are promising, and there is therefore an urgent need for randomised control studies such as the TRIGGER trial to confirm the safety of this strategy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".