Prevalence and characterization of breakthrough pain in patients with cancer in Spain: the CARPE-DIO study
Bibliographic record
Abstract
We aimed to evaluate the prevalence, characteristics and impact of breakthrough pain (BTP) in patients with cancer attending the main specialties involved in the diagnosis and management of BTP in Spain using a multicenter, observational, cross-sectional, multidisciplinary study. Investigators had to record all patients seen at the clinic during 1 month, determine whether the patients had cancer pain, and apply the Davies algorithm to ascertain whether the patients were suffering from BTP. Of the 3,765 patients with cancer, 1,117 (30%) had cancer-related pain, and of these patients, 539 had BTP (48%, 95%CI:45-51). The highest prevalence was found in patients from palliative care (61%, 95%CI:54-68), and the lowest was found in those from hematology (25%, 95%CI:20-31). Prevalence varied also according to sex and type of tumor. According to the Alberta Breakthrough Pain Assessment Tool duration, timing, frequency, location, severity, quality, causes, and predictability of the BTP varied greatly among these patients. BTP was moderate (Brief Pain Inventory [BPI]-severity median score = 5.3), and pain interference was moderate (BPI-interference median score = 6.1) with a greater interference with normal work, general activity, and enjoyment of life. Patients with BTP showed a mean ± standard deviation score of 28.5 ± 8.0 and 36.9 ± 9.5 in the physical and mental component, respectively, of the SF-12 questionnaire. In conclusion, prevalence of BTP among patients exhibiting cancer-related pain is high. Clinical presentation is heterogeneous, and therefore, BTP cannot be considered as a single entity. However, uniformly BTP has an important impact on a patient's functionality, which supports the need for early detection and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".