Bariatric surgery and all‐cause mortality: A methodological review of studies using a non‐surgical comparator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: Non-randomized studies on bariatric surgery have reported large reductions in mortality within 6-12 months after surgery compared with non-surgical patients. It is unclear whether these findings are the result of bias. STUDY DESIGN AND SETTING: We searched PubMed to identify all non-randomized studies investigating the effect of bariatric surgery on all-cause mortality compared with non-surgical patients. We assessed these studies for potential confounding and time-related biases. We conducted bias analyses to quantify the effect of these biases. RESULTS: We identified 21 cohort studies that met our inclusion criteria. Among those, 11 were affected by immortal time bias resulting from the misclassification or exclusion of relevant follow-up time. Five studies were subject to potential confounding bias because of a lack of adjustment for body mass index (BMI). All studies used an inadequate comparator group that lacked indications for bariatric surgery. Bias analyses to correct for potential confounding from BMI shifted the effect estimates towards the null [reported hazard ratio (HR): 0.78 vs. bias-adjusted HR: 0.92]. Bias analyses to correct for the presence of immortal time also shifted the effect estimates towards the null (adjustment for 2-year wait time: reported HR: 0.57 vs. bias-adjusted HR: 0.81). CONCLUSION: Several important sources of bias were identified in non-randomized studies of the effectiveness of bariatric surgery versus non-surgical comparators on mortality. Future studies should ensure that confounding by BMI is accounted for, considering the choice of the comparator group, and that the design or analysis avoids immortal time bias from the misclassification or exclusion.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it