Personalized Pain Goals and Responses in Advanced Cancer Patients
Bibliographic record
Abstract
OBJECTIVE: To assess the personalized pain intensity goal (PPIG), the achievement of a personalized pain goal response (PPGR), and patients' global impression (PGI) in advanced cancer patients after a comprehensive pain and symptom management. DESIGN: Prospective, longitudinal. SETTING: Acute pain relief and palliative/supportive care. SUBJECTS: 689 advanced cancer patients. METHODS: Measurement of Edmonton Symptom Assessment Score (ESAS) and personalized pain intensity goal (PPIG) at admission (T0). After a week (T7) personalized pain goal response (PPGR) and patients' global impression (PGI) were evaluated. RESULTS: The mean PPIG was 1.33 (SD 1.59). A mean decrease in pain intensity of - 2.09 was required on PPIG to perceive a minimal clinically important difference (MCID). A better improvement corresponded to a mean change of - 3.41 points, while a much better improvement corresponded to a mean of - 4.59 points. Patients perceived a MCID (little worse) with a mean increase in pain intensity of 0.25, and a worse with a mean increase of 2.33 points. Higher pain intensity at T0 and lower pain intensity at T7 were independently related to PGI. 207 (30.0%) patients achieved PPGR. PPGR was associated with higher PPIG at T0 and T7, and inversely associated to pain intensity at T0 and T7, and Karnofsky level. Patients with high pain intensity at T0 achieved a favorable PGI, even when PPIG was not achieved by PPGR. CONCLUSION: PPIG, PPGR and PGI seem to be relevant for evaluating the effects of a comprehensive management of pain, assisting decision-making process according to patients' expectations. Some factors may be implicated in determining the individual target and the clinical response.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".