Leveraging Digital Infrastructure for Data Analysis: An Example in Bariatric Surgery
Bibliographic record
Abstract
BACKGROUND: The digitalization of healthcare information provides hospitals with the ability to gain insight into patterns and associations pertaining to disease and management. Using bariatric patient data as an example provided an opportunity to explore the potential of electronic medical record (EMR) data to generate insights. OBJECTIVE: The aim of this study was to extract EMR data pertaining to bariatric patient information as a means to explore predictive factors of weight loss post-bariatric surgery. METHODS: We conducted a retrospective cohort study of patients undergoing bariatric surgery between January 1, 2018, and April 30, 2019, at Humber River Hospital. Multiple linear regression was used to examine whether age, pre-surgery body mass index (BMI), comorbidities and mental health disorders predicted higher weight loss 6 months following bariatric surgery. RESULTS: A total of 502 patients were included in the final analysis. Age (ß = 0.04 [95% CI 0.01, 0.06], p = 0.005), baseline BMI (ß = -0.16 [95% CI -0.19, -0.13], p = <0.0001) and diabetes (ß = 0.82 [95% CI 0.23, 1.42], p = 0.007) were associated with weight loss six months post-bariatric surgery. CONCLUSION: EMRs are a rich source of data with the potential to generate insights that can lead to improved care.
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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 | 0.040 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".