Patient Satisfaction Following Laparoscopic Umbilical Hernia Repair Using a “Two-Port” Technique
Bibliographic record
Abstract
NOTICE: THIS ARTICLE HAS BEEN RETRACTED. Background: Measuring patient satisfaction is important to help improve health service delivery and improve outcomes. The aim of this study is to evaluate patient satisfaction with laparoscopic umbilical hernia repair and determine overall satisfaction with referral, outpatient consultation, pre-assessment clinic attendance, and post-operative care. Methods: This was a retrospective study of 52 patients undergoing laparoscopic umbilical hernia repair. Each patient completed an extensive self-administered questionnaire distributed at a scheduled follow-up appointment 3 months following the operation. Results: The response rate was 86.5%. Most patients (77.8%) were referred from their general practitioner. Patient satisfaction with surgical outcome was very high at > 98%, and the overall patient satisfaction from being listed for surgery to discharge was > 95%. Patient satisfaction was significantly associated with the patient being referred to the hospital by a general practitioner (GP). Conclusions: Patient satisfaction is an important health outcome, and understanding the domains of satisfaction, as well as their relative importance to patients, is necessary to improve overall quality of patient care. Laparoscopic umbilical hernia repair using a “two-port†technique is an effective procedure with excellent results and an extremely high rate of patient satisfaction. J Curr Surg. 2017;7(4):49-52 doi: https://doi.org/10.14740/jcs333w Â
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".