Bibliographic record
Abstract
PURPOSE OF REVIEW: Cancer pain remains inadequately treated, despite internationally accepted management guidelines and a myriad of treatment options. Risk factors for undertreatment are reviewed, along with possible explanations. Recent studies documenting the scope of the problem as well as investigating solutions are discussed with clinical-practice recommendations outlined. RECENT FINDINGS: Women over 65 years of age representative of a cultural minority, with earlier stage disease, cared for at home, and with high-school education or less are at highest risk of having uncontrolled cancer pain. Optimal treatment is impeded by patients' maladaptive beliefs, nonadherence, underreporting or miscommunication with caregivers; from a healthcare provider perspective, it may be due to inadequate assessment, documentation, knowledge, and communication. Emerging data support the vital influence of lay caregivers on appropriate pain management. Although home-education programs may decrease pain and improve quality of life, there are also less intensive approaches deliverable by individuals to holistically address pain. SUMMARY: Prospective study of barriers to both delivery and receipt of adequate pain management is needed, as the majority of published literature is based on survey studies. Treatment must be individualized based on clinical circumstances and patient wishes, with the goal of maximizing function and quality of life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".