Long-term Follow-up of Patients With Hernia Using the Hernia-Specific Quality-of-Life Mobile App: Feasibility Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Hernia repair is one of the most common surgical procedures; however, the long-term outcomes are seldom reported due to incomplete follow-up. OBJECTIVE: The aim of this study was to examine the use of a mobile app for the long-term follow-up of hernia recurrence, complication, and quality-of-life perception. METHODS: A cloud-based corroborative system drove a mobile app with the HERQL (Hernia-Specific Quality-of-Life) questionnaire built in. Patients who underwent hernia repair were identified from medical records, and an invitation to participate in this study was sent through the post. RESULTS: The response rate was 11.89% (311/2615) during the 1-year study period, whereas the recurrence rate was 1.0% (3/311). Causal relationships between symptomatic and functional domains of the HERQL questionnaire were indicated by satisfactory model fit indices and significant regression coefficients derived from structural equational modeling. Regarding patients' last hernia surgeries, 88.7% (276/311) of the patients reported them to be satisfactory or very satisfactory, 68.5% (213/311) of patients reported no discomfort, and 61.1% (190/311) of patients never experienced mesh foreign body sensation. Subgroup analysis for the most commonly used mesh repairs found that mesh plug repair inevitably resulted in worse symptoms and quality-of-life perception from the group with groin hernias. CONCLUSIONS: The mobile app has the potential to enhance the quality of care for patients with hernia and facilitate outcomes research with more complete follow-up.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".