Characterization of cancer pain syndromes (PS) seen at a Comprehensive Cancer Center (CCC) and pain response (PR) to palliative care consultation (PCC)
Bibliographic record
Abstract
8551 Background: Comparison of cancer PS across settings is challenging due to differences in prognostic features. Data from 1 CCC participating in a multi-site international study of a pain classification system is presented to characterize cancer PS & response to PCC. Methods: The Edmonton Classification System for Cancer Pain was completed by prospective chart review to characterize PS of 100 consecutive hospitalized patients (pts) seen in PCC. Pts were followed until major PR, hospital discharge or death. Major PR was defined as <2 p.r.n. opioid doses/d & pain intensity (PI) <3/10 for 3 consecutive days (d). Results: 85% of pts had pain (n=85), with age 62.9+13.3, 47.1% male & KPS 44.5+23.1. The most common tumor diagnoses were lung (24.7%) & GU (21.2%). Pts were followed for a median of 4 d (0–27). 39% achieved a major PR. Except for steroids (49.4%) & anticonvulsants (29.4%), other adjuvant analgesic use was all <10%. Pain-associated features: *Numeric Rating Scale 0–10, 10=worst suggestivie of alcoholism + Mean morphine equivalent dailydose On univariate analysis, older age (p=.006), lower initial PI (p=.003), lower final PI (p=.001) & lower final MEDD (p=.002) were significantly associated with achieving major PR. On multivariate analysis, lower initial PI (p=.03) & lower final MEDD (p=.02) retained significance for achieving major PR. Conclusions: Only 39%of pts with cancer pain seen in PCC achieve a major PR by discharge or death. Despite aggressive opioid titration, 61% do not achieve a major PR & require better pain management. Potential strategies for achieving improved PR include earlier PCC, identification of more sensitive prognostic variables &critical evaluation of targeted therapies. [Table: see text] No significant financial relationships to disclose.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".