Development and Evaluation of a Web Site to Improve Recovery From Hysterectomy
Bibliographic record
Abstract
Following surgery, information received upon discharge for recovery at home varies depending on the hospital, and the information is typically given to the patient all at once rather than timed to the recovery process. To address these information challenges, a Web site to help women recovering at home after hysterectomy was developed and evaluated. The Web site was designed to guide the hysterectomy patient through her postsurgical recovery by providing timely and relevant information tailored to the patient's stage of recovery. The Web site required patients to complete a checkup assessing 18 symptoms related to their recovery, and advice was given on how to deal with any symptom the patient had. The Web site also provided care tips specific to the patient's day of recovery along with general information regarding hysterectomy and recovery. Thirty-one women participated in the evaluation, which consisted of preoperative and postoperative surveys as well as a telephone interview. Results indicated that patients frequently used and were highly satisfied with the Web site. Patients reported that the Web site was easy to use and informative, helped to guide their recovery, reduced worry and anxiety, and helped to inform decisions of when and how to contact health professionals. Based on the findings, the Web site represents a potentially cost-effective means to aid women recovering from hysterectomy.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".