4 M's to make sense of evidence – Avoiding the propagation of mistakes, misinterpretation, misrepresentation and misinformation
Bibliographic record
Abstract
Osteopaths are expected to keep up to date with research evidence relevant to their clinical practice and to integrate this knowledge with their own experience and their patients' values and preferences. One of the potential challenges when engaging with research is to make sense of it, to decide if it is trustworthy, and if it is applicable to the complex and context-sensitive nature of clinical practice and the care of individual people. Clinicians are increasingly exposed to (deliberate and undeliberate) misinformation and overstatements which propagate easily, including via social media. This masterclass aims to facilitate critical thinking and engagement in research for clinicians to make better-informed decisions with their patients. It was developed to support osteopaths facing these questions with the aim of empowering them to judge research themselves, detect common fallacies in the conduct and reporting of different research designs, and to increase researchers' accountability. Ultimately, we hope that by reading and considering the guidance and examples in this paper, clinicians will be better equipped to optimise the use of their (and their patients') time when facing potential sources of evidence. Mistakes, misinterpretation, misrepresentation and misinformation are discussed for each of these methods/methodologies: case reports, clinical trials, qualitative research, and reviews.
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 | 0.205 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier 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".