Is it possible to measure our patients' pain?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to describe and discuss the main instruments for assessing chronic musculoskeletal pain and its associated symptoms and syndromes. The treatment of patients with chronic pain, regardless of the underlying disease, presents challenges inherent to its multidimensional nature. One of the main challenges is how to measure the outcomes of interventions. The most common forms of measurement are analog scales. These are considered unidimensional because they assess only pain intensity, without considering other clinical aspects. Questionnaires with multidimensional scales have the advantage of capturing not only pain intensity but also other accompanying phenomena, such as the degree of disability, emotional aspects, and even social and occupational impacts. Regarding multidimensional instruments for pain assessment, we cite the Brief Pain Inventory and the McGill Pain Questionnaire. Other multidimensional instruments include: Clinically Aligned Pain Assessment (CAPA) Tool, Defense and Veterans Pain Rating Scale, Geriatric Pain Measure, Pain Impact Questionnaire (PIQ-6), Pain Monitor, and Short Form-36 Bodily Pain Scale (SF-36 BPS). As for more specific questionnaires, there are the Fibromyalgia Impact Questionnaire, the Fibromyalgia Scale, and the Central Sensitization Inventory. Among the symptoms that most frequently accompany chronic pain, fatigue and sleep disturbances stand out. These have specific questionnaires for their assessment and are also included in more generic instruments. In conclusion, the search for a simple and applicable metric for chronic pain is still far from being achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it