Bibliographic record
Abstract
Pain is an important concern in patients with cancer who are receiving active treatment and in long-term cancer survivors. It is one of the most feared aspects of cancer, and it can have a major adverse impact on quality of life. It has long been recognized that untreated or undertreated pain is common in patients with cancer, with little evidence of recent improvement. 1,2 Given the many advances in knowledge regarding cancer pain and its management over the past decade, this Special Series issue was assembled to provide readers with an update on the current understanding of the biology of cancer pain, the biology and mechanisms of action of the opioids used to treat cancer pain, challenges in the management of cancer pain, and evidence-based multidisciplinary approaches to management of cancer pain. We hope that the articles in this Special Series issue will enhance clinician understanding of cancer pain and its treatment and provide practical approaches to managing pain in patients with cancer. Hui and Bruera 3 outline “A Personalized Approach to Assessing and Managing Pain in Patients With Cancer.” They provide a practical yet evidence-based approach to personalized pain assessment and management in the clinic. They also discuss a paradigm shift in pain management, outlining a multistep approach that includes systematic screening, comprehensive pain assessment, characterization of pain, identification of personal modulators of pain expression, documentation of personalized pain goals, and implementation of a multidisciplinary treatment plan with subsequent customized longitudinal monitoring. Their article summarizes an expert clinician’s approach to pain management and provides practical tables and figures that will be helpful to the clinician who manages cancer pain. Falk and Dickenson 4 discuss the biology of pain in patients with cancer, with a special focus on bone pain, in their article entitled “Pain and Nociception: Mechanisms of Cancer-Induced Bone Pain.” The authors review mechanisms of cancer pain, stating that it is a “complex pain state involving components of both inflammatory and neuropathic pain, but also displaying elements that appear to be unique to cancer pain.” They discuss cancer pain as unpredictable, with highly variable intensity, which makes it difficult to manage. They also review mechanisms of acute pain, neuropathic pain, inflammatory pain, and complex cancer pain, providing informative figures that will help clinicians understand the biology of pain and may help guide the selection of management approaches. Gavril Pasternak 5 reviews “Opiate Pharmacology and Relief of Pain,” a complex area that is presented in a clear fashion. Pasternak states that opioids “selectively impact the hurt of nociceptive stimuli”; they act through activation of a pain modulating system consisting of endogenous opioid peptides and their receptors. He discusses three classes of opioid/receptors—mu, delta, and kappa—and suggests that individual responses to specific opioids may reflect differences in the biology of these receptors. Pasternak discusses important issues such as opioid cross tolerance, benefits, and risks of opioid rotation and the growing understanding of mu receptor subtypes, introducing the
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".