We Must Improve How Clinicians Interpret the Study Results of Benefit and Harm
Bibliographic record
Abstract
Knowledge translation (KT) has become a pivotal part of the research world. 1 For research to have meaning, the results must be communicated to others who can appreciate and use the new information in some relevant capacity. From the clinician’s perspective, KT is an essential ingredient to closing the gap between best evidence and clinical practice. 2 How the presenters at a medical conference communicate the results of clinical studies to practising physicians is the subject of an article by Allen et al in the current issue of the Canadian Journal of General Internal Medicine (CJGIM). The authors point out that the terminology and format by which results are communicated may be less than ideal. What is also striking is the limited ability amongst clinicians for understanding the formats commonly used for communicating study results. It is this latter issue that clearly needs to be addressed in medical schools and residency programs. Moreover, the ideal solution to improving the communication of statistical information to practitioners, so that they can make fully informed therapeutic decisions, is not to narrow the format to a select number of “ideal” parameters, but rather to expand the ability of the recipients to understand as many of these relevant terms as possible.
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 | 0.427 | 0.736 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.042 | 0.062 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.023 | 0.039 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".