<scp>A</scp>lberta <scp>B</scp>reakthrough <scp>P</scp>ain <scp>A</scp>ssessment <scp>T</scp>ool: A validation multicentre study in cancer patients with breakthrough pain
Bibliographic record
Abstract
BACKGROUND: Cancer-related breakthrough pain (BTP) is a common and quite challenging pain syndrome, with significant impact on quality of life. To date, no widely recognized and validated tool for the diagnosis and evaluation of BTP exists. The Alberta Breakthrough Pain Assessment Tool (ABPAT) underwent a validation process during its development, but no experience of its implementation in clinical practice has been reported. METHODS: ABPAT was tested in a cohort of cancer patients suffering from chronic severe cancer-related pain in order to assess its acceptability and efficacy as a tool for the characterization of BTP. RESULTS: A total of consecutive 249 patients from seven different centres were included in a 2-month study period and all completed the questionnaire; 231 out of the 249 (92.8%) stated that questions were easily understandable and 217 out of the 249 (87.1%) stated that the tool allowed to explain extensively the BTP problem. Physician-patient correlation tests about baseline BTP intensity and BTP relief by medication showed statistical significance at the level of p = 0.001 and p = 0.0001, respectively. Evaluation of the efficacy of BPT medication revealed a 78.2% of patients declaring a good relief from BTP, with a significant reduction of mean BTP numeric rating scale score (p = 0.0001), but only 55.9% of patients responded to be satisfied about time for onset of the relief. CONCLUSIONS: In this study, ABPAT resulted to be a well-accepted tool for BTP assessment and characterization in a relatively large cohort of cancer patients. It is effective in discovering the unmet needs of cancer patients and in exploring the outcomes of BTP treatment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".