When People with Opioid-Induced Constipation Speak: A Patient Survey
Bibliographic record
Abstract
INTRODUCTION: Opioid-induced constipation (OIC) is a common consequence of opioid use for chronic pain. OIC creates problems for patients independent of their pain syndromes, in addition to threatening pain treatment effectiveness. Healthcare practitioners need to be alert to how patients talk about OIC so that it is not missed. Using a survey mechanism, we sought patient expressions of the personal impact OIC imposes on how they are able to live their lives and on meanings that symptom relief would produce. METHODS: We used an online survey asking adults with OIC about quality of life implications of OIC and focused on open-ended text responses to questions about personal impacts of straining and meanings attached to OIC symptom relief. Participants were from the US, Canada, UK, Germany, Sweden, and Norway. RESULTS: A survey of 513 people with OIC produced 280 text responses concerning the impacts of straining on quality of life and 469 text responses on the meaning OIC symptom relief would confer. Text responses about the quality of life impacts of straining often included explicit descriptions conveying physical, psychological, or practical problems. Text responses about the meaning conferred from OIC symptom relief primarily concentrated around freedom from the constraints that OIC can impose. CONCLUSIONS: Patients are willing and able to comment on the problems OIC cause them, using a variety of terms and phrases. Their comments concerning impacts on their lives will often refer to physical consequences, psychological effects, or practical implications. These insights provide healthcare practitioners guidance on how to engage patients about OIC.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".