Consumer focused education on acetaminophen side effects, inadequate outcomes and weaning for individuals with low back pain: protocol for a feasibility study
Bibliographic record
Abstract
Background: Prescription and over the counter medications, such as paracetamol, account for a significant proportion of the direct and indirect costs in managing low back pain (LBP). Existing research has not only questioned the efficacy of paracetamol use for musculoskeletal conditions such as LBP, but also its safety. No previous study has investigated the feasibility of a pharmacological education tool for individuals using paracetamol to manage their LBP. The aims of this study are to investigate: (1) the acceptability and experience of participants with the pharmacological education tool, (2) feasibility of recruitment, data collection and outcome measure completion, and (3) participant’s willingness to participate in a randomised control trial. Methods: This will be a single group repeated measures study design recruiting individuals from community organisations. Included participants will be over 18 years, experiencing acute, chronic or recurrent episodes of LBP and self-report consumption of paracetamol for pain relief weekly for at least one month. This study will be open for recruitment from June 2021 to August 2021. Conclusions: Our findings will inform the feasibility of conducting a larger randomised controlled trial. This study will be judged as feasible to proceed to a full trial based if, 1) recruitment sources are each able to enrol at least 20 participants into the study within three months of initial advertisement, 2) the majority of participants find the study and intervention experience as acceptable, and 3) there is less than 20% of missing data for the primary outcomes, and a minimum of 85% follow-up rate for enrolled participants.
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.
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 | 0.050 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.111 | 0.022 |
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".