Prevalence and characteristics of breakthrough cancer pain in an outpatient clinic in a Catalan teaching hospital: incorporation of the Edmonton Classification System for Cancer pain into the diagnostic algorithm
Bibliographic record
Abstract
BACKGROUND: Breakthrough cancer pain (BTcP) is defined according to its principal characteristics: high intensity, short time interval between onset and peak intensity, short duration, potential recurrence over 24 h and non-responsiveness to standard analgesic regimes. The Edmonton Classification System for Cancer Pain (ECS-CP) is a classification tool that evaluates different dimensions of pain. The aim of this study was to measure prevalence and the main characteristics of BTcP in a sample of advanced cancer patients and to explore the complexity observed when ECS-CP is incorporated into BTcP diagnostics algorithm. METHODS: Descriptive prevalence study (Retrospective chart review). Davies' algorithm was used to identify BTcP and ECS-CP was used to recognize appropriate dimensions of pain. The study was conducted in a sample of advanced cancer patients attending hospital outpatient clinic in Lleida, Spain. 277 patients were included from 01/01/2014 to 31/12/2015. No direct contact was made with participants. The following information was extracted from the palliative care outpatient clinic database: age, gender, civil status, cognitive impairment status, functional performance status and variables related to tumour. Only BTcP cases were included. RESULTS: Prevalence of BTcP was 39.34% (63.9% men). Mean of age was 68.2 years. Main diagnosis was lung cancer (n = 154; 31.6%). Metastases were diagnosed in 83% of the sample. 138 patients (49.8%) were diagnosed with 1 type of BTcP and 139 (50.2%) were diagnosed with more than one type of BTcP. In total, 488 different types of BTcP were recorded (mean 1.75 ± 0, 9), 244 of these types (50%) presented a component of neuropathic pain. Addictive behaviour, measured through CAGE test, was present in 29.2% (N = 81) of the patients and psychological distress was present in 40.8% (n = 113). CONCLUSIONS: Prevalence of BTcP (39.34%) is similar to the one reflected in the existing literature. Study results indicate that the routine use of ECS-CP in a clinical setting allows us to detect more than one type of BTcP as well as additional complexity associated with pain (neuropathic, addictive behavior and psychological distress).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".