Factors Associated with Loss to Follow-up among Cervical Cancer Patients in Rwanda
Bibliographic record
Abstract
Background: Cervical cancer is among the most common cancers affecting women globally. Where treatment is available in low- and middle-income countries, many women become lost to follow-up (LTFU) at various points of care. Objective: This study assessed predictors of LTFU among cervical cancer patients in rural Rwanda. Methods: We conducted a retrospective study of cervical cancer patients enrolled at Butaro Cancer Center of Excellence (BCCOE) between 2012 and 2017 who were either alive and in care or LTFU at 12 months after enrollment. Patients are considered early LTFU if they did not return to clinic after the first visit and late LTFU if they did not return to clinic after the second visit. We conducted two multivariable logistic regressions to determine predictors of early and late LTFU. Findings: Of 652 patients in the program, 312 women met inclusion criteria, of whom 47 (15.1%) were early LTFU, 78 (25.0%) were late LTFU and 187 (59.9%) were alive and in care. In adjusted analyses, patients with no documented disease stage at presentation were more likely to be early LTFU vs. patients with stage 1 and 2 when controlling for other factors (aOR: 14.93, 95% CI 6.12-36.43). Patients who travel long distances (aOR: 2.25, 95% CI 1.11, 4.53), with palliative care as type of treatment received (aOR: 6.65, CI 2.28, 19.40) and patients with missing treatment (aOR: 7.99, CI 3.56, 17.97) were more likely to be late LTFU when controlling for other factors. Patients with ECOG status of 2 and higher were less likely to be late LTFU (aOR: 0.26, 95% CI 0.08, 0.85). Conclusion: Different factors were associated with early and later LTFU. Enhanced patient education, mechanisms to facilitate diagnosis at early stages of disease, and strategies that improve patient tracking and follow-up may reduce LTFU and improve patient retention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".