Patient-Initiated Follow-Up in Ovarian Cancer
Bibliographic record
Abstract
This study aimed to assess the feasibility of patient-initiated follow-up (PIFU) in combination with regular tumour marker monitoring as an alternative to conventional hospital follow-up for ovarian cancer survivors. Women who had recently completed treatment for ovarian cancer and had a raised pre-treatment tumour marker were recruited. Participants were allocated to PIFU (intervention group) or conventional hospital follow-up (control group) according to their own preference. Both groups had regular tumour marker monitoring. The change in fear of cancer recurrence (FCR) score as measured by the FCR inventory, and the supportive care need (SCN) scores as measured by the SCN survey at baseline and at 6 months between PIFU and hospital follow-up were compared. Out of 64 participants, 37 (58%) opted for hospital follow-up and 27 (42%) opted for PIFU. During the 6-month study period, there was no significant difference in the change of FCR between the two groups (p = 0.35). There was a significant decrease in the sexuality unmet needs score in the intervention group from baseline to 6-month FU (mean difference −8.7, 95% confidence interval −16.1 to −1.4, p = 0.02). PIFU with tumour marker monitoring is a feasible follow-up approach in ovarian cancer survivorship care. FCR and SCN were comparable between PIFU and conventional hospital follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".