Something Must Be Wrong with the Implementation of Cancer-pain Treatment Guidelines. A Lesson from Referrals to a Pain Clinic
Bibliographic record
Abstract
OBJECTIVE: The World Health Organization's (WHO) guidelines for cancer pain management were intentionally made simple in order to be widely implemented by all physicians treating cancer patients. Referrals to pain specialists are advised if pain does not improve within a short time. The present study examined whether or not a reasonable use of the WHO guideline was made by non-pain specialists prior to referral of patients with cancer-related pain to a pain clinic. METHODS: Cancer patients referred to a pain specialist completed several questionnaires including demographics, medical history, and cancer-related pain; the short-form McGill Pain Questionnaire (SF-MPQ); and the Short Form Health Survey SF-12. Data from referral letters and medical records were obtained. Treatments recommended by pain specialists were recorded and categorized as "unjustified" if they were within the WHO ladder framework, or "justified" if they included additional treatments. RESULTS: Seventy-three patients (44 women, 29 men) aged 55 years (range, 25-85) participated in the study. Their pain lasted for a mean of 6 (1-192) months. Mean pain intensity scores on a 0-10 numerical rating scale were 7 (2-10) at rest and 8 (3-10) upon movement. Most patients complied with their referring physician's recommendations and consumed opioids. Adverse events were frequent. No significant correlation was found between the WHO analgesic medication step used and mean pain levels reported. There were 63 patient referrals (85%) categorized as "unjustified," whereas only 11 patients (15%) required "justified" interventions. CONCLUSIONS: These findings imply that analgesic treatment within the WHO framework was not reasonably utilized by non-pain specialists before referring patients to pain clinics.
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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| 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".