Personalized Prehabilitation Improves Tolerance to Chemotherapy in Patients with Oesophageal Cancer
Bibliographic record
Abstract
Background: Prehabilitation programmes aim to optimise patients before and after cancer treatment including surgery. Previous studies in surgical patients demonstrate that prehabilitation improves pre-operative fitness and overcomes the negative impact of neoadjuvant chemotherapy on fitness. The aim of this study was to assess the impact of prehabilitation on the tolerance of neoadjuvant chemotherapy in patients with oesophageal cancer. Methods: Patients with oesophageal or gastroesophageal junction (GOJ) cancer from two oncology centres were retrospectively included in the present comparative cohort study; one provided a multimodal prehabilitation programme and one did not offer any prehabilitation. Tolerance of chemotherapy, defined as completion of the full chemotherapy regime as per protocol, was compared between the two groups. Results: In terms of participants, 92 patients were included in this study, 47 patients in the prehabilitation cohort and 45 in the control cohort. Compared with the control group, the prehabilitation group demonstrated an improved rate of chemotherapy completion (p = 0.029). In multivariate analysis, participation in prehabilitation was significantly associated with an improved rate of chemotherapy completion. Conclusion: The findings of this exploratory study suggest that prehabilitation is associated with better tolerance for chemotherapy. Further research is needed to establish the long-term impact of prehabilitation on oncological outcomes.
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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 | 0.000 | 0.002 |
| 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".