Challenges in managing cancer pain
Bibliographic record
Abstract
Abstract Effective pain control is a key factor in cancer management, and is primarily achieved through drug therapy. Despite the World Health Organization (WHO) guidelines for cancer pain management, many cancer patients still do not receive adequate analgesia. Lack of knowledge and misconceptions about opioid treatment is a key contributing factor, along with shortcomings in the WHO guidelines themselves. Evidence shows that starting treatment with strong opioids can provide significantly greater benefits than being treated according to the WHO recommended ‘analgesic ladder’ — a sequential escalation of treatment. There is, therefore, a need for alternative treatment strategies that optimise drug selection, dose and methods of administration. Although widely used, drug delivery through the oral routes is not always acceptable for cancer patients with oral and gastrointestinal problems, and as such a range of administration routes, including transdermal, transmucosal and intranasal routes should be considered. Promising clinical outcomes with treatments such as transdermal fentanyl and intranasal fentanyl spray lend support to the opinion that strong opioids, used in appropriate formulations and doses, play an important role in the care of patients with severe pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".