Breakthrough cancer pain: The importance of the right treatment at the right time
Bibliographic record
Abstract
BACKGROUND: Confusion remains over the definition of breakthrough cancer pain (BTcP) potentially leading to delayed diagnosis and treatment. METHODS: An on-line survey was conducted in four EU countries among relevant healthcare professionals and cancer patients diagnosed with BTcP. The roles of healthcare professionals (HCPs) were examined and their knowledge and use of available medications recorded. Patients were questioned on how BTcP affected their lives and on the medications they had received/were receiving. RESULTS: There was a 'time lag' of 58 and 13 weeks in Germany and Spain respectively between the initial diagnosis of BTcP and its treatment. Four in ten oncologists across the four countries considered themselves not fully confident in their choice of the appropriate therapy. A quarter of patients in Germany, Italy and Spain and four in ten in France were treated only with increased dosages of the therapy already prescribed for their background pain - often morphine. Almost another quarter received morphine in addition to their treatment for background pain. Oncologists indicated a need for faster-acting treatments revealing a potential lack of awareness of rapid onset oral opioids and patients expressed a desire for more effective pain relief and better psychological support. CONCLUSIONS: There is a need for a universal definition of BTcP to facilitate earlier and more accurate diagnosis. It is essential that BTcP is treated immediately on diagnosis with therapies that more closely mirror its temporal characteristics to ensure that patients' desire for more effective pain relief is fulfilled. SIGNIFICANCE: Many cancer patients suffered episodes of BTcP needlessly over many months due to missed diagnosis. Even after diagnosis, many physicians were not fully confident in their choice of 'rescue' therapy which perhaps is not surprising given the very low level of awareness of treatment guidelines, both national and international.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".