Exploring the Potential of an Eye Tissue Donor Reporting App in Enhancing the Procurement of Corneal Donors: Mixed Methods Observational Study
Bibliographic record
Abstract
BACKGROUND: The availability of donated eye tissue saves and enhances vision in transplant recipients; however, the current demand for tissue surpasses the available supply. Corneal donor shortages lead to increased wait times, delayed surgeries, prolonged visual impairment, and increased inconvenience to patients requiring eye tissue transplantation. A web-based application was previously developed to facilitate easy and intuitive submission of potential donor information. OBJECTIVE: The primary objectives of this study were to assess health care professionals' attitudes toward the potential application and evaluate its effectiveness based on user feedback and donor registrations through the application. METHODS: Researchers used a mixed methods approach, commencing with a literature review to identify challenges associated with donor procurement. Stakeholder interviews were conducted to gauge health care professionals' perspectives regarding the application. User feedback was collected through questionnaires, surveys, and interviews to assess the application's usability and impact. An assessment of the reported potential donors and questionnaire responses were analyzed. RESULTS: The final version of the application successfully reported 24 real cornea donors. Among 64 health care providers who used the application to communicate about potential donors, 32 of them submitted trial entries exclusively for testing purposes. The remaining 8 health care professionals reported potential donors; however, these individuals did not meet the donor qualification criteria. The majority of participants found the application user-friendly and expressed their readiness to use it in the future. Positive ratings were assigned to the layout, appearance, purpose, and specific features of the application. Respondents highlighted the automatic sending of notifications via SMS text messages and the integration of all necessary documents for donor qualification and tissue collection as the most valuable functions of the application. CONCLUSIONS: The study indicates that donor reporting applications offer promising solutions to enhance tissue donor procurement. This application streamlined the reporting process, reduced paperwork, facilitated communication, and collected valuable data for analysis.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".