Cancer-Related Pain Management in Suitable Intrathecal Therapy Candidates: A Spanish Multidisciplinary Expert Consensus
Bibliographic record
Abstract
A consensus is needed among healthcare professionals involved in easing oncological pain in patients who are suitable candidates for intrathecal therapy. A Delphi consultation was conducted, guided by a multidisciplinary scientific committee. The 18-item study questionnaire was designed based on a literature review together with a discussion group. The first-round questionnaire assessed experts' opinion of the current general practice, as well as their recommendation and treatment feasibility in the near future (2-3-year period) using a 9-point Likert scale. Items for which consensus was not achieved were included in a second round. Consensus was defined as ≥75% agreement (1-3 or 7-9). A total of 67 panelists (response rate: 63.2%) and 62 (92.5%) answered the first and second Delphi rounds, respectively. The participants were healthcare professionals from multiple medical disciplines who had an average of 17.6 (7.8) years of professional experience. A consensus was achieved on the recommendations (100%). The actions considered feasible to implement in the short term included effective multidisciplinary coordination, improvement in communication among the parties, and an assessment of patient satisfaction. Efforts should focus on overcoming the barriers identified, eventually leading to the provision of more comprehensive care and consideration of the patient's perspective.
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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 | 0.034 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".