Pain Assessment for Cancer Patients Based on Their Pain Descriptions (part 2)-Preliminary Study for Selecting Adequate Analgesics and Adjuvant Analgesics using APQ-
Bibliographic record
Abstract
Pain assessment is important in treating the pain of cancer patients and choosing adequate analgesics for this purpose. Though pain is defined as a subjective phenomenon, it is necessary to evaluate the words chosen by cancer patients to describe their pain objectively. In our previous study (part 1), We developed the Aichi Prefectural Society of Hospital Pharmacists Pain Questionnaire (APQ) based on the McGill Pain Questionnaire (MPQ), a tool for measuring pain based on words used to describe pain.In order to evaluate pain in thirty-one cancer patients in ten hospitals using the APQ, we investigated the relationship between the words used by patients to describe pain and pain quality (equivalent to the subclasses in APQ) and opioid responsiveness. In addition, we tried to select adequate adjuvant analgesics based on the words for pain in the APQ through a search of the literature. Words used to describe pain and pain quality for pain that is responsive or non-responsive to opioids could be inferred from the seventy-eight pain words in the APQ.These findings suggest that we can choose adequate medication based on an evaluation of the patient's pain using the APQ and relieve cancer pain successfully.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".