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Record W2783373910 · doi:10.14740/jcs333w

Patient Satisfaction Following Laparoscopic Umbilical Hernia Repair Using a “Two-Port” Technique

2017· article· en· W2783373910 on OpenAlexvenueno aff
Ghassan Almaimani

Bibliographic record

VenueJournal of Current Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineUmbilical herniaPatient satisfactionReferralHernia repairHerniaAttendanceSurgeryLaparoscopyGeneral surgeryNursing

Abstract

fetched live from OpenAlex

NOTICE: THIS ARTICLE HAS BEEN RETRACTED.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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.366
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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