Stereoscopic three‐dimensional visualisation technology in anatomy learning: A meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: The features that contribute to the apparent effectiveness of three-dimensional visualisation technology [3DVT] in teaching anatomy are largely unknown. The aim of this study was to conduct a systematic review and meta-analysis of the role of stereopsis in learning anatomy with 3DVT. METHODS: The review was conducted and reported according to PRISMA Standards. Literature search of English articles was performed using EMBASE, MEDLINE, CINAHL EBSCOhost, ERIC EBSCOhost, Cochrane CENTRAL, Web of Science and Google Scholar databases until November 2019. Study selection, data extraction and study appraisal were performed independently by two authors. Articles were assessed for methodological quality using the Medical Education Research Study Quality Instrument and the Cochrane Collaboration's tool for assessing the risk of bias. For quantitative analysis, studies were grouped based on relative between-intervention differences in instructional methods and type of control conditions. RESULTS: A total of 3934 citations were obtained of which 67 underwent a full-text review. Ultimately, 13 randomised controlled trials were included in the meta-analysis. When interactive, stereoscopic 3D models were compared to interactive, monoscopic 3D models within a single level of instructional design, for example isolating stereopsis as the only true manipulated element in the experimental design, an effect size [ES] of 0.53 (95% confidence interval [CI] 0.26-0.80; P < .00001) was found. In comparison with 2D images within multiple levels of instructional design, an effect size of 0.45 (95% CI 0.10-0.81; P < .002) was found. Stereopsis had no effect on learning when utilised with non-interactive 3D images (ES = -0.87, 95% CI -2.09-0.35; P = .16). CONCLUSION: Stereopsis is an important distinguishing element of 3DVT that has a significant positive effect on acquisition of anatomical knowledge when utilised within an interactive 3D environment. A distinction between stereoscopic and monoscopic 3DVT is essential to make in anatomical education and research.
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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 | 0.025 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.047 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".