INCREASING EXPERTISE IN PATIENT-CENTRED BREAKTHROUGH CANCER PAIN MANAGEMENT USING RAPID-ONSET OPIOIDS: FOCUS ON SUBLINGUAL FENTANYL CITRATE
Bibliographic record
Abstract
Cancer pain represents a very frequent symptom in cancer patients; it is one of the factors which most impacts their quality of life, and clinicians often experience issues regarding its management. This study focuses on Breakthrough cancer pain which is defined as a transient episode of severe pain in a context of adequately controlled background pain. It has a higher prevalence in advanced disease and in palliative care settings; however, it is present throughout all phases of cancer treatment and follow-up. It should not be considered as a single entity, as it is classified as spontaneous, incident volitional or incident non-volitional. To allow a proper diagnosis, clinicians may leverage specific useful tools, such as the Breakthrough Pain Assessment Tool, and patient-reported outcome measures, using a patient-centred care approach. Breakthrough cancer pain treatment requires Rapid-Onset Opioids, namely rapid-active fentanyl formulations having an onset of effect of less than 15 minutes and a short duration of effect. There are different Rapid-Onset Opioids with different routes of administrations which clinicians can choose according to patient characteristics and preferences. For instance, sublingual fentanyl citrate has an innovative formulation which provides a very rapid onset of action, approximately 6 minutes, giving rapid pain relief; intranasal Rapid-Onset Opioids could be preferable in patients with mucositis or with xerostomia. Opioid use in background cancer pain and Breakthrough cancer pain are a cornerstone of treatment. Perspective: In the treatment of cancer patients, pain management is of paramount importance, given its impact on the quality of life. A correct diagnosis of Breakthrough cancer pain and proper framing of the patient suffering from it allows for the best management of this issue by using Rapid-Onset Opioids.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".