Published randomized controlled trials of surveillance in cancer patients - a systematic review
Bibliographic record
Abstract
With solid tumor cancer survivorship increasing, the number of patients requiring post-treatment surveillance also continues to increase. This highlights the need for evidence-based cancer surveillance guidelines. Ideally, these guidelines would be based on combined high-quality data from randomized controlled trials (RCTs). We present a systematic review of published cancer surveillance RCTs in which we sought to determine the feasibility of data pooling for guideline development. We carried out a systematic search of medical databases for RCTs in which adult patients with solid tumors that had undergone surgical resection with curative intent and had no metastatic disease at presentation, were randomized to different surveillance regimens that assessed effectiveness on overall survival (OS). We extracted study characteristics and primary and secondary outcomes, and assessed risk of bias and validity of evidence with standardized checklist tools. Our search yielded 32,216 articles for review and 18 distinct RCTs were included in the systematic review. The 18 trials resulted in 23 comparisons of surveillance regimens. There was a highlevel of variation between RCTs, including the study populations evaluated, interventions assessed and follow-up periods for the primary outcome. Most studies evaluated colorectal cancer patients (11/18, [61%]). The risk of bias and validity of evidence were variable and inconsistent across studies. This review demonstrated that there is tremendous heterogeneity among RCTs that evaluate effectiveness of different postoperative surveillance regimens in cancer patients, rendering the consolidation of data to inform high-quality cancer surveillance guidelines unfeasible. Future RCTs in the field should focus on consistent methodology and primary outcome definition.
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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 | 0.040 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".