Long-term patient-reported distress in locally advanced cervical cancer patients treated with definitive chemoradiation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To evaluate longitudinal patient-reported distress in cervical cancer patients treated with definitive chemoradiation (CRT). MATERIALS AND METHODS: Between 2011 and 2016, consenting cervical cancer patients treated with definitive CRT who completed ≥ 2 revised Edmonton Symptom Assessment System (ESAS-r) questionnaires at clinical visits, including baseline, were included. A linear mixed model was used to assess the longitudinal trend in ESAS-r. A minimal clinically important difference (MCID) for total ESAS-r score was defined as a change of 3-points for improvement and 4-points for deterioration. The proportion of patients with an MCID over time was described using moving averages. To test for changes, mixed effects logistic models were fitted, each of which included patient-specific random intercepts and random slopes. RESULTS: 67 patients were eligible for analysis (736 ESAS-r assessments). Median (range) follow-up was 24 months (range: 15-45) and compliance at 12 months was 60% (40/67). There was a significant decrease in ESAS-r scores over time. Baseline ESAS-r was strongly predictive of ESAS-r at follow-up (p < 0.001). The proportion of patients with an MCID for improvement from baseline significantly increased over time (p < 0.001) and the proportion with an MCID for deterioration significantly decreased over time (p < 0.001). No predictors for distress were found. CONCLUSIONS: Long-term cervical cancer survivors experience distress that significantly improves over time to an extent expected to be clinically meaningful for patients. Implementing cervical cancer specific patient-reported outcome tools into practice could better inform patient needs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".