The effect of surveillance intensity on long-term survival following curative surgical treatment for solid cancers: A systematic review and meta-analysis
Bibliographic record
Abstract
BackgroundThe number of cancer survivors continues to increase due to advancements in cancer treatment. Consequently, the number of patients requiring post-operative surveillance protocols have increased, highlighting the need to develop post-operative surveillance protocols that increase survival benefit while remaining both cost- and resource-effective. MethodsWe carried out a comprehensive and systematic literature search of medical databases for randomized controlled trials (RCTs) in which patients with solid tumors 18 years of age or older that had undergone surgical resection with curative intent and had no metastatic disease at presentation, were randomized to different surveillance regimens to assess the effect on overall survival (OS). According to a priori definitions, surveillance programs were classified as (1) biological test, (2) frequency, (3) imaging, or (4) practitioner type. We carried out a pooled effect size estimate on risk ratios (RR) to evaluate the effect of more intensive versus less intensive surveillance strategies in each of the surveillance program categories on overall survival (OS). ResultsOur search yielded a total of 32,216 articles for review. Following all screening stages, 18 distinct RCTs were included in the systematic review. Most studies evaluated colorectal cancer patients (11/18, [61%]). Twenty-one comparisons from the 18 trials were included in the meta-analysis, with no significant difference in OS for any of the more intensive surveillance program categories analysed. One comparison evaluated biological test interventions and therefore, a meta-analysis was not conducted. Six comparisons were classified as frequency interventions with a combined RR of 0.96 (95% CI: 0.79-1.16). Eleven comparisons were classified as imaging interventions with a combined RR of 0.99 (95% CI: 0.91-1.08). Two comparisons were classified as practitioner type interventions with a combined RR of 1.106 (95% CI: 0.64-1.91). No difference in OS was observed in any of the cancer type subgroup analyses. Conclusion The cost of cancer care is steadily rising due to the growing number of cancer survivors, leading to questions in the ability of healthcare systems to meet the needs of patients. This study demonstrated that there is currently no evidence to support any kind of more intensive surveillance in solid cancers after treatment when considering OS as the primary outcome. However, due to persistent clinical equipoise and advancements in surveillance options, further large RCTs are required to evaluate the most optimal surveillance protocol for individual cancer types.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".