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
Chronic pain is one of the common reasons patients visit the doctor, During these appointments, physicians and all medical care professionals should evaluate patients for comorbidities such as depression and substance dependency. The purpose of treating chronic noncancer pain (CNCP) is not always to eliminate the pain; therefore, it is important to communicate about the treatment plan and target. Important points to discuss include reducing the pain, improving quality of life, and increasing the patient's function.In Europe, many studies reported a significant influence of CNCP on different aspects of quality of life. Chronic pain negatively changes the patients’ perception of general health, interferes in daily activities which make patients participate less in these activities, and isolates patients from family and friends which increases the risk of depression. Chronic pain has an economic impact. These impacts include lost work days. In a 6-month study, the average lost work days were 7.8; although, about 22% of patients in this study had a minimum of 10 missed work days. These numbers increased when the patient had a major depressive disorder.[4]There are pharmaceutical and non-pharmaceutical treatments for CNCP. In the management of CNCP, physicians should always consider all treatment options, and if possible, to try to use the non-addictive options, especially when the patient has a history of substance abuse.[5]Management of CNCP ideally includes different specialties including the primary care physician, psychiatrist, addiction specialist, pain specialist, psychologist, and pharmacist to provide the best treatment plan and goal for the patient.[6] An addiction specialist is one of the most important specialists to involve to monitor patients using drugs with dependency potential, identify the possible relapse, and evaluate these patients throughout the treatment.
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 | 0.001 | 0.000 |
| 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 | 0.002 |
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