Characteristics of Breakthrough Pain and Its Impact on Quality of Life in Terminally Ill Cancer Patients
Bibliographic record
Abstract
Purpose. This study aimed to characterize breakthrough pain (BTP) and investigate its impact on quality-of-life (QoL) in terminally-ill cancer patients. Similarities and differences between high and low predictable BTP were also tested. Methods. Secondary analysis of a multicenter longitudinal observational study included 92 patients at their end-of-life. BTP was assessed with a short form of the Italian version of the Alberta Breakthrough Pain Assessment Tool. QoL was assessed with the Palliative Outcome Scale (0-40). Patients were stratified by self-reported BTP predictability into unpredictable BTP (never or rarely able to predict BTP) and predictable BTP (sometimes to always able to predict BTP). Results. In all, 665 BTP episodes were recorded (median 0.86 episodes/day). A median duration of 30 minutes and a median peak intensity score of 7 out of 10 were reported. Time to peak was <10 minutes, 10 to 30 minutes, and ≥30 minutes in 267 (41.1%), 259 (39.9%), and 30 (4.6%) of the episodes, respectively. Onset of relief occurred after a median of 30 minutes. Time to peak ( P < .001) and duration ( P = .046) of BTP was shorter in patients with predictable pain ( n = 31), who usually were younger than those with unpredictable pain ( P = .03). The mean (SD) QoL score was 14.6 (4.6). No difference in QoL between patients with predictable and unpredictable BTP was found ( P = .49). Conclusions. In terminally-ill cancer patients, BTP is a severe problem with a negative impact on QoL and has different characteristics according to its predictability.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".