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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".