Quality of Follow-up
Bibliographic record
Abstract
OBJECTIVE: We aim to systematically review the bariatric surgery literature with regards to adequacy of patient follow-up, meeting the McMaster criteria of ≥80% follow-up. BACKGROUND: Loss to follow-up is a major concern and can potentially bias the outcome and interpretation of a study. The quality of follow-up in bariatric surgery is quite variable with recent systematic reviews criticizing the field for its lack of overall follow-up. METHODS: A complete search of PubMed was performed. Literature was restricted to a range of 5 years (2007-2012), English language, and publications listed in PubMed. The McMaster Evidence-based Criteria for High Quality Studies was used to assess the follow-up data adequacy and a logistic meta-regression was performed to identify factors associated with high quality follow-up studies. RESULTS: Ninety-nine published manuscripts were included. For follow-up at study end, only 40/99 (40.4%) of papers had adequate patient follow-up, 42/99 (42.4%) failed to meet the McMaster criteria and 17/99 (17.2%) failed to report any follow-up results. On average, 31% were lost to follow-up at the study's end. Only shorter study duration, and if the study was performed in the US, were associated with studies meeting the McMaster criteria. CONCLUSIONS: Only 40% of studies in the bariatric surgery literature meet criteria for adequate follow-up. On average, studies have 30% of patients lost to follow-up at the stated end-point. Identified study characteristics associated with high quality follow-up included shorter study duration and studies performed in the US.
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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 | 0.246 | 0.504 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".