A Pilot Study in the Use of Activity Trackers for Assessing Response to Palliative Radiotherapy
Bibliographic record
Abstract
Purpose Radiation therapy (RT) has been a frequently used treatment for painful bone metastasis. The aim of this study was to determine the feasibility of using activity trackers (AT) to assess the patient prognosis and the effects of palliative RT. Methods and materials Twelve patients planned to receive palliative RT for axial metastases and were prospectively recruited to participate in this pilot clinical trial. The patients were eligible if there was no intent to change the analgesic medications prior to or within seven days of palliative RT. All the patients were lent a Misfit FlashTM activity tracker (Misfit, Burlingame, California, United States of America) and were asked to wear it from the time of baseline assessment prior to RT until seven days after RT. The patients completed the European Organisation for Research and Treatment of Cancer quality of life (QOL) questionnaire (EORTC-QLQ C30) and the Short Form Brief Pain Inventory (SF-BPI) before the treatment and at days seven, 30 and 90 after completion of the RT. The patients' Karnofsky Performance Status (KPS) was assessed at each visit. The patients' overall survival at the end of the RT was recorded. Average daily steps before and after RT were compared using paired Wilcoxon signed-rank test. The patients' overall survival was estimated using the Kaplan-Meier curve and analyzed using the Log-Rank test. Results The median age of the patients was 62 years (range: 40-79 years). Of the 12 patients, there were five prostate, three breasts, three lungs, and one colon cancer-related patients. Six patients received 20 Gray (Gy) in five fractions and six received 8 Gy in one fraction. By day seven, post-RT, there was a 30% (p <0.02) reduction in the patients' daily activity level. There was no correlation between improvements in the QOL or with the level of pain and with the number of daily steps. While baseline KPS was not prognostic of the patient survival, the patients who on average took more than 7800 steps per day prior to RT lived significantly (p=0.034) longer than those who were less active. Conclusions The baseline activity level is associated with the patient prognosis. A significant decline in the physical activity was observed at one week after palliative RT. The use of activity trackers was to prognosticate and to monitor the patients' response to the palliative RT and should be evaluated further.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".