YouTube Videos to Create a “Virtual Hospital Experience” for Hip and Knee Replacement Patients to Decrease Preoperative Anxiety: A Randomized Trial
Bibliographic record
Abstract
BACKGROUND: With declining reimbursement to health care systems, face-to-face time between patients and providers to optimize preoperative education and counseling may be challenging. OBJECTIVE: Because high patient anxiety prior to surgery has been linked to more severe and persistent pain after joint replacement surgery, the Orthopedic Surgery Department at Mayo Clinic in Florida created a playlist of 16 YouTube videos aimed at creating a virtual hospital experience for primary total hip and knee joint replacement patients. A randomized trial was then performed to evaluate the potential impact of viewing this playlist on preoperative anxiety. METHODS: Each patient completed a Generalized Anxiety Disorder (GAD) score assessment at the time of the routine preoperative clinic visit and then randomized based on his/her gender, type of surgery, and initial GAD score to either the control group of standard education (education at face-to-face clinical visits as well as printed educational materials) or the treatment group (standard education plus access to the YouTube playlist). On the morning of the patient's surgery, the same survey was repeated. Of the 65 patients who consented to participate in the study, 53 completed the study (82%) with 28 of 29 (97% completed) in the control group and 25 of 36 (69% completed) in the treatment group. RESULTS: Overall, the results showed a trend toward less anxiety in patients who viewed the YouTube videos; this was exhibited by a reduction in the median GAD score by 1 point. This trend is more clearly present in patients with high preoperative anxiety (predominantly women), as seen in the reduction of the median GAD score by 6 points in the treatment group. CONCLUSIONS: Although our experience is limited, our results indicate that a series of tailored videos may decrease patient anxiety preoperatively. We recommend further exploration of both this concept and the use of social media tools in preoperative patient education. TRIAL REGISTRATION: Clinicaltrials.gov NCT02546180; http://clinicaltrials.gov/ct2/show/NCT02546180 (Archived by WebCite at http://www.webcitation.org/6f6y0Dw7d).
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".