Elective Laparoscopic Paraesophageal Hernia Repair Leads to an Increase in Life Expectancy Over Watchful Waiting in Asymptomatic Patients
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to perform an updated Markov analysis to determine the optimal management strategy for patients with an asymptomatic paraesophageal hernia (PEH): elective laparoscopic hernia repair (ELHR) versus watchful waiting (WW). BACKGROUND: Currently, it is recommended that patients with an asymptomatic PEH not undergo repair based on a 20-year-old Markov analysis. The current recommendation might lead to preventable hospitalizations for acute PEH-related complications and compromised survival. METHODS: A Markov model with updated variables was used to compare life-years (L-Ys) gained with ELHR versus WW in patients with a PEH. One-way sensitivity analyses evaluated the robustness of the analysis to alternative data inputs, while probabilistic sensitivity analysis quantified the level of confidence in the results in relation to the uncertainty across all model inputs. RESULTS: At age 40 to 90, ELHR led to greater life expectancy than WW, particularly in women. The gain in L-Ys (2.6) was greatest in a 40-year-old woman and diminished with increasing age. Sensitivity analysis showed that alternative values resulted in modest changes in the difference in L-Ys, but ELHR remained the preferred strategy. Probabilistic analysis showed that ELHR was the preferred strategy in 100% of 10,000 simulations for age 65, 98% for age 80, 90% for age 85, and 59% of simulations in 90-year-old women. CONCLUSIONS: This updated analysis showed that ELHR leads to an increase in L-Ys over WW in healthy patients aged 40 to 90 years with an asymptomatic PEH. In this new paradigm, all patients with a PEH, regardless of symptoms, should be referred for the consideration of elective repair to maximize their life expectancy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".