Effects of de-escalated bisphosphonate therapy on the Functional Assessment of Cancer Therapy-Bone Pain, Brief Pain Inventory and bone biomarkers
Bibliographic record
Abstract
BACKGROUND: The Brief Pain Inventory (BPI) and Functional Assessment of Cancer Therapy-Bone Pain (FACT-BP) are commonly used measures of patient reported pain outcomes. We report on the performance of the FACT-BP in comparison to the BPI within a small, randomized trial. METHODS: Patients with biochemically defined low risk bone metastases were randomized to 4 weekly (control arm) or 12 weekly (de-escalating arm) pamidronate for 1 year. FACT-BP, BPI and serum markers of bone turnover were recorded at baseline and weeks 12, 24, 36 and 48. Mixed effects models were used to compare scores over time between arms. Correlation coefficients were calculated to evaluate the association between FACT-BP and BPI scores, as well as with markers of bone turnover. RESULTS: Nineteen patients were randomized to each study arm. Pain scores determined by the two instruments were moderately to highly correlated with each other. Baseline C-telopeptide (CTx) level was correlated with baseline FACT-BP and BPI scores. Baseline bone-specific alkaline phosphatase showed a non-significant association with pain scores. There were no correlations between the markers of bone turnover and pain scores at week 12. CONCLUSIONS: In the current study the FACT-BP and BPI correlated well with each other, and with baseline CTx. The possibility of linking subjective pain scores with objective biomarkers of response requires more investigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".