Diabetes Life Expectancy Prediction Model Inputs and Results From Patient Surveys Compared With Electronic Health Record Abstraction: Survey Study
Bibliographic record
Abstract
Background: Prediction models are being increasingly used in clinical practice, with some requiring patient-reported outcomes (PROs). The optimal approach to collecting the needed inputs is unknown. Objective: Our objective was to compare mortality prediction model inputs and scores based on electronic health record (EHR) abstraction versus patient survey. Methods: Older patients aged ≥65 years with type 2 diabetes at an urban primary care practice in Chicago were recruited to participate in a care management trial. All participants completed a survey via an electronic portal that included items on the presence of comorbid conditions and functional status, which are needed to complete a mortality prediction model. We compared the individual data inputs and the overall model performance based on the data gathered from the survey compared to the chart review. Results: For individual data inputs, we found the largest differences in questions regarding functional status such as pushing/pulling, where 41.4% (31/75) of participants reported difficulties that were not captured in the chart with smaller differences for comorbid conditions. For the overall mortality score, we saw nonsignificant differences (P=.82) when comparing survey and chart-abstracted data. When allocating participants to life expectancy subgroups (<5 years, 5-10 years, >10 years), differences in survey and chart review data resulted in 20% having different subgroup assignments and, therefore, discordant glucose control recommendations. Conclusions: In this small exploratory study, we found that, despite differences in data inputs regarding functional status, the overall performance of a mortality prediction model was similar when using survey and chart-abstracted data. Larger studies comparing patient survey and chart data are needed to assess whether these findings are reproduceable and clinically important.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".