Bending a tree while it is young: Getting pain management training on the academic map
Bibliographic record
Abstract
he authors of the paper have to be congratulated for their idea to focus on the important issue of training in management of acute and chronic pain.Probably most urologists and other clinical specialists around the world would agree that structured training in the management of pain during medical school or residency draws only very limited attention.Guidelines for general pain management are available from some pain societies, but also urology-specific guidelines with regard to pain exist. 1,2But guidelines only accomplish their purpose if they are implemented in daily practice, and most importantly transferred into medical education programs.The management of patients with pain, especially in the case of chronic pain, is often a difficult task.Many hospitals in industrialized countries now have acute pain management and palliative care services, but these are not available to everybody at all times.Therefore, the individual treating physician often is the first and most important part of the team and must understand different aspects of treatment.In the optimal case for the management of chronic pain, a multidisciplinary team should care for the patient, focussing on all aspects of pain, including psychological and functional consequences using an integrative approach. 3As with other difficult medical treatments, opioid use for pain is criticized for its inappropriate application.It is important to know how and when to use opioids; however, it is equally crucial to know when they should be avoided.Guidance on all of this is important and should be part of structured training.An experiential or "learning by doing" approach for most of pain management issues is insufficient.If treatment selection is based on habits rather than evidence, optimal management will often be lacking.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".