Risk Stratified Follow-Up for Endometrial Cancer: The Clinicians’ Perspective
Bibliographic record
Abstract
Risk-stratified follow-up for endometrial cancer (EC) is being introduced in many cancer centres; however, there appears to be diversity in the structure and availability of schemes across the UK. This study aimed to investigate clinicians' and clinical specialist nurses' (CNS) experiences of follow-up schemes for EC, including patient-initiated follow-up (PIFU), telephone follow-up (TFU) and clinician-led hospital follow-up (HFU). A mixed-methods study was conducted, consisting of an online questionnaire to CNSs, an audience survey of participants attending a national "Personalising Endometrial Cancer Follow-up" educational meeting, and qualitative semi-structured telephone interviews with clinicians involved in the follow-up of EC. Thematic analysis identified three main themes to describe clinicians' views: appropriate patient selection; changing from HFU to PIFU schemes; and the future of EC follow-up schemes. Many participants reported that the COVID-19 pandemic impacted EC follow-up by accelerating the transition to PIFU/TFU. Overall, there was increasing support for non-HFU schemes for patients who have completed primary treatment of EC; however, barriers were identified for non-English-speaking patients and those who had communication challenges. Given the good long-term outcome associated with EC, greater focus is needed to develop resources to support patients post-treatment and individualise follow-up according to patients' personal needs and preferences.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".