P.126 Enhancing patient understanding of spinal conditions through advanced imaging platforms
Bibliographic record
Abstract
Background: In spite of the shift towards “personalized medicine,” ambulatory medicine lags behind the cutting edge technology employed in non-medical fields to convey information in unique ways to enhance customer interactions. Furthermore, the complex nature of neurosurgical concepts can be difficult to convey within the confines of a short outpatient visit. These factors, coupled with potentially long wait times, can limit a patient’s engagement in the treatment process. We propose that application of advanced video platforms will empower patients to feel that neurosurgical concepts are accessible and understandable and enable the face-to-face time with the physician to begin at a more sophisticated level, ultimately improving patient engagment. Methods: 3D modeling, animation, and video game design were used in conjuction with tablet computers and VR headsets to create a video-driven “choose-your-own-adventure style patient experience” with initial use during waiting times prior to face-to-face interaction with the neurosurgery providers. Results: 3D modeling, animation, and virtual reality were successfully implemented in the Northwestern Medicine neurosurgery clinic with positive impact on patient engagement, including preliminary improvements in multiple patient satisfaction/”Likelihood to Recommend” scores. Conclusions: Advanced imaging platforms, including 3D modeling, animation, and virtual reality show great promise in improving patient engagement, patient retention, and “Likelihood to Recommend” scores.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".