465Simplifying the Differences between Causal and Prediction Analyses
Bibliographic record
Abstract
Abstract Focus of Presentation Multivariable regression models can be used to answer a variety of clinical questions. The two main objectives of regression models are to either 1) understand an association between one or more exposures and an outcome; or 2) predict future outcomes based on certain exposures or variables. To simplify this, we will consider the former causal analysis, and the latter prediction analysis. This presentation will explain the steps in model development and assessment using a clinical case study, highlighting the similarities and differences. This presentation is aimed at trainees. Findings The key differences between causal and prediction models include: the purpose and research questions, power calculations, variable selection, model specification, testing model fit, and the desired outcome of each model. The case study demonstrates these differences, while working through a causal and prediction model with similar clinical questions. Conclusions/Implications It is important for researchers to consider the purpose of their research question and to tailor the model accordingly. This will guide the model development and interpretation, which are different for causal and prediction analyses. Key messages A thorough understanding of the types of models available, their assumptions, and the process of model development and assessment is essential to conducting research that is valid and applicable to the clinical environment, enabling knowledge translation.
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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 | 0.102 | 0.362 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".