Identification and prediction of health‐related quality of life trajectories after a prostate cancer diagnosis
Bibliographic record
Abstract
The aim of our study was to identify physical and mental health-related quality of life (HRQoL) trajectories after a prostate cancer diagnosis and systematically characterize trajectories by behaviours and prognostic factors. Prostate cancer survivors (n = 817) diagnosed between 1997 and 2000 were recruited between 2000 and 2002 into a prospective repeated measurements study. Behavioural/prognostic data were collected through in-person interviews and questionnaires. HRQoL was collected at three post-diagnosis time-points, approximately 2 years apart using the Short Form (SF)-36 validated questionnaire. To identify physical and mental HRQoL trajectories, group-based trajectory modelling was undertaken. Differences between groups were evaluated by assessing influential dropouts (mortality/poor health), behavioural/prognostic factors at diagnosis or during the follow-up. Three trajectories of physical HRQoL were identified including: average-maintaining HRQoL (32.2%), low-declining HRQoL (40.5%) and very low-maintaining HRQoL (27.3%). In addition, three trajectories for mental HRQoL were identified: average-increasing HRQoL (66.5%), above average-declining HRQoL (19.7%) and low-increasing HRQoL (13.8%). In both physical and mental HRQoL, dropout from mortality/poor health differed between trajectories, thus confirming HRQoL and mortality were related. Furthermore, increased Charlson comorbidity index score was consistently associated with physical and mental HRQoL group membership relative to average maintaining groups, while behaviours such as time-varying physical activity was associated with physical HRQoL trajectories but not mental HRQoL trajectories. It was possible to define three trajectories of physical and mental HRQoL after prostate cancer. These data provide insights regarding means for identifying subgroups of prostate cancer survivors with lower or declining HRQoL after diagnosis whom could be targeted for interventions aimed at improving HRQoL.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".