Assessment and classification of cancer breakthrough pain: A systematic literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Temporal variations in cancer pain intensity are highly prevalent, and are often difficult to manage. However, the phenomenon is not well understood: several definitions and approaches to classification and bedside assessment of cancer breakthrough pain (BTP) have been described. The present study is a systematic review of published literature on cancer BTP to answer the following questions: which terms and definitions have been used; are there validated assessment tools; which domains of BTP do the tools delineate, and which items do they contain; how have assessment tools been applied within clinical studies; and are there validated classification systems for BTP. A systematic search of the peer-reviewed literature was performed using five major databases. Of 375 titles and abstracts initially identified, 51 articles were examined in detail. Analysis of these publications indicates a range of overlapping but distinct definitions have been used to characterize BTP; 42 of the included papers presented one or more ways of classifying BTP; and while 10 tools to assess patients' experience of BTP were identified, only 2 have been partially validated. We conclude that there is no widely accepted definition, classification system or well-validated assessment tool for cancer-related breakthrough pain, but there is strong concurrence on most of its key attributes. With further work in this area, an internationally agreed upon definition and classification system for cancer-related breakthrough pain, and a standard approach on how to measure it, hold the promise to improve patient care and support research in this poor-prognosis cancer pain syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it