Protocol for the REBOUND study: a cohort study to uncover fundamental mechanisms of accelerated ageing and impaired resilience following cancer surgery and treatment
Bibliographic record
Abstract
Abstract Background Ageing is a heterogeneous process, which is associated with heterogeneous resilience in older people. Cancer surgery and treatment may be associated with accelerated ageing in some older people; studying this process will improve understanding to enable treatments to prevent adverse effects on physical and cognitive function. Methods This study will recruit 172 participants aged 65 years and older scheduled to undergo elective colorectal surgery for cancer from two hospital sites (Guy’s and St Thomas’ NHS Foundation Trust and University Hospitals Birmingham NHS Foundation Trust). Assessments will be performed preoperatively, days 1–3 postoperatively, 30 days postoperatively, and 90 days postoperatively. These will include in-depth clinical phenotyping including handgrip strength, Short Physical Performance Battery, muscle ultrasound, cognitive tests, Electroencephalography, questionnaires including quality of life, and physical activity using remote devices. Serial blood and stool specimens will be collected across timepoints to measure underlying hallmarks of ageing including inflammation, dysbiosis, macroautophagy, cellular senescence, epigenetic alterations, mitochondrial dysfunction, and stem cell exhaustion. A machine learning approach will be utilised to evaluate the associations between trajectories in clinical and physiological parameters and fundamental biological processes. Discussion This study represents an exciting collaboration between clinicians, fundamental scientists, and experts in machine learning. It offers the opportunity to characterise and understand complex pathways to enable future clinical trials directed towards the prevention of accelerated ageing through a stratified medicine approach.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.090 | 0.038 |
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".