Building bridges from research outcomes to clinical practice decisions
Bibliographic record
Abstract
Practitioners may face difficulties implementing research results into practice. Seven examples of common knowledge translation barriers for clinicians are presented, and suggestions are offered for building effective communication bridges. Changes in how research results are reported and interpreted across different practice contexts can improve orthodontic care. These include (a) attention to the expected benefit that includes estimates of both likely clinical value and probability of occurrence, (b) cost considerations, (c) generalizability across contexts that require interpretation adjustments, (d) measures of effect size in addition to measures of statistical significance, (e) determination of the largest relative sources of variance in the reported results, (f) estimating probabilities that lead to practice actions, and (g) conversion of research descriptions to values that impact practice decisions. Examples of improved communication relevant to clinicians are provided that can be used to build stronger bridges between orthodontic research and practice. Although advances in orthodontic research rigor have been noted, journal articles would benefit from more clinician-friendly descriptions of results and their impact.
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 | 0.710 | 0.837 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.051 | 0.088 |
| Open science | 0.014 | 0.057 |
| Research integrity | 0.025 | 0.043 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".