The COVID-19 pandemic: An opportunity to rethink and harmonise the frequency of follow-up visits for patients with early stage breast cancer
Bibliographic record
Abstract
PURPOSE: While routine, in-person follow-up of early-stage breast cancer patients (EBC) after completion of initial treatment is common, the COVID-19 pandemic has resulted in unprecedented changes in clinical practice. A systematic review was performed to evaluate the evidence supporting different frequencies of routine follow-up. METHODS: MEDLINE and the Cochrane Collaboration Library were searched from database inception to July 16, 2020 for randomized controlled trials (RCTs) and prospective cohort studies (PCS) evaluating different frequencies of routine follow-up. Citations were assessed by pairs of independent reviewers. Risk of Bias (RoB) was assessed using the Cochrane RoB tool for RCTs and the Newcastle-Ottawa Quality Assessment Scale for Cohort Studies. Findings were summarized narratively. RESULTS: The literature search identified 3316 studies, of which 7 (6 RCTs and 1 PCS) were eligible. Study endpoints included; quality of life (QoL; 5 RCTs and 1 PCS), disease free survival (DFS) (1 RCT), overall survival (OS) (1 RCT) and cost-effectiveness (1 RCT). The results showed reduction in follow-up frequency had no adverse effect on: QoL (6 studies, n = 920), DFS (1 trial, n = 472) or OS (1 trial, n = 472), but improved cost-effectiveness (1 trial, n = 472). Four RCTs specifically examined follow-up on-demand versus scheduled follow-up visits and found no statistically significant differences in QoL (n = 544). CONCLUSION: While no evidence-based guidelines suggest that follow-up of EBC patients improves DFS or OS, routinely scheduled in-person assessment is common. RCT data suggests that reduced frequency of follow-up has no adverse effects.
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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 | 0.134 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| 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".