The brief pain inventory : revealing the impact of cancer pain.
Bibliographic record
Abstract
The science of pain assessment was born in the 1970s when visual analogue and numbered scales were adapted by psychologists from educational research settings and applied to patients with pain. This period also saw the publication of the well known McGill Pain Questionnaire that examined use of verbal descriptors for both the sensory and affective dimensions of pain. Within a short time, research in pain was progressing quickly, affirming the quote attributed to Lord Kelvin: “when you can measure what you are speaking about, and express it in numbers, you know something about it”. Publication of the Brief Pain Questionnaire in 1983, by Daut and colleagues,1 marked the start of the modern era of cancer-pain measurement and inspired better recognition of the effect of pain on the lives of patients with cancer. The Brief Pain Questionnaire also helped to focus attention on the neglected area of cancer-pain management; WHO did not publish the first guidelines advocating regular administration of opioids to patients with cancer pain until 1986.2 This treatment approach was regarded with scepticism by the medical community at that time, and is a view which sometimes prevails today. The 1983 paper reported on a new questionnaire—tested in more than 1200 patients with cancer who attended the Wisconsin Cancer Center and found to be acceptable, reliable, and valid. Although the Brief Pain Questionnaire incorporated existing numbered scales, it was an important step forward in pain measurement because it introduced two new concepts. The first idea reflected the simple observation that the intensity of cancer pain can vary, and measuring both maximum and average pain within the previous week is important. This understanding helped to identify the concept of cancer breakthrough pain. The second concept recognised that pain interferes with aspects of everyday living such as walking, sleeping, mood, and relations with other people. Measuring this interference is essential to understand the experience of cancer pain and monitor the effectiveness of treatment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".