Narrative bias (“spin”) is common in randomised trials and systematic reviews of cannabinoids for pain
Bibliographic record
Abstract
ABSTRACT: We define narrative bias as a tendency to interpret information as part of a larger story or pattern, regardless of whether the facts support the full narrative. Narrative bias in title and abstract means that results reported in the title and abstract of an article are done so in a way that could distort their interpretation and mislead readers who had not read the whole article. Narrative bias is often referred to as "spin." It is prevalent in abstracts of scientific papers and is impactful because abstracts are often the only part of an article read. We found no extant narrative bias instrument suitable for exploring both efficacy and safety statements in randomized trials and systematic reviews of pain. We constructed a 6-point instrument with clear instructions and tested it on randomised trials and systematic reviews of cannabinoids and cannabis-based medicines for pain, with updated searches to April 2021. The instrument detected moderate or severe narrative bias in the title and abstract of 24% (8 of 34) of randomised controlled trials and 17% (11 of 64) of systematic reviews; narrative bias for efficacy and safety occurred equally. There was no significant or meaningful association between narrative bias and study characteristics in correlation or cluster analyses. Bias was always in favour of the experimental cannabinoid or cannabis-based medicine. Put simply, reading title and abstract only could give an incorrect impression of efficacy or safety in about 1 in 5 papers reporting on these products.
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 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 | 0.085 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".