Pain Assessment for Cancer Patients Based on Their Pain Descriptions (Part 1)-Development and Evaluation of Methods of Pain Assessment-
Bibliographic record
Abstract
Cancer pain has a number of physical and psychological components. It is usually defined as a subjective phenomenon since only the sufferer experiences it and because of this, cancer pain is difficult to evaluate. Many cancer patients suffer from their pain, and an important part of relieving it is determining the intensity and characteristics of such pain through pain assessment. We considered that the words chosen by patients to describe their pain were useful for its assessment and had potential value as a diagnostic adjunct.We evaluated methods of pain assessment for cancer patients with regard to the following objectives : 1) To find an adequate pain assessment instrument for cancer patients for clinical use and to develop the Aichi Prefectural Society of Hospital Pharmacists Pain Questionnaire (APQ) based on the McGill Pain Questionnaire (MPQ), 2) To investigate the relationship between the etiology of pain and words related to pain using APQ and 3) To analyze the relationship between the etiology of pain and the words related to pain by collecting seventy clinical cases. We predicted whether morphine would be effective or not based on the relationship between changes in morphine doses and changes in verbal pain descriptions made by patients. Our findings indicated that pain assessment by APQ is useful means of selecting adequate therapeutics for pain relief.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".