Reliability of the Brief Pain Inventory (BPI) in Patients with Bone Metastases
Bibliographic record
Abstract
Goals of Work. To examine the reliability between the Brief Pain Inventory (BPI) pain intensity and pain interference scores in patients with bone Metastases. To also measure the correlation of pain interference with both Karnofsky Performance Score (KPS) and enjoyment of life.Patients and Methods. Patients with bone Metastases receiving palliative radiotherapy for bone pain rated their intensity and interference of pain using the BPI before, 10 consecutive days after, and one month following treatment. All patients were prescribed 8 mg of dexamethasone prior to treatment in hopes of preventing a pain flare. Reliability was assessed in terms of internal consistency using Cronbach's coefficient alpha at baseline and follow-up. Spearman's correlation coefficient measured the correlation between pain interference and KPS or enjoyment of life at baseline.Main Results. Between January 2004 and October 2005, thirty-three patients (10 female and 23 male) were accrued. Twenty-two were available for one-month follow-ups. Over the 10-day follow-up, the average consistencies of worst, average and current pain were very similar (0.951, 0.952 and 0.949). For all pain scores, Cronbach's alphas began with lower baseline values and stabilized after the 5th day of follow-up. At one-month, the reliabilities remained similar for all pain intensities (0.950, 0.939, and 0.939). Current pain generally had lower reliability than the other pain intensity scales. KPS had strong correlation with normal work (r = -0.42), general activity (r = -0.41) and walking ability (r = -0.35). Enjoyment of life showed strong correlation with all other pain interference items measured.Conclusions. Based on 10-day and one-month post-treatment scores, the BPI provides good reliability between pain intensity and pain interference scores in patients with bone Metastases.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".