Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia
Bibliographic record
Abstract
There are an increasing number of "big data" studies in anesthesia that seek to answer clinical questions by observing the care and outcomes of many patients across a variety of care settings. This Readers' Toolbox will explain how to estimate the influence of patient factors on clinical outcome, addressing bias and confounding. One approach to limit the influence of confounding is to perform a clinical trial. When such a trial is infeasible, observational studies using robust regression techniques may be able to advance knowledge. Logistic regression is used when the outcome is binary (e.g., intracranial hemorrhage: yes or no), by modeling the natural log for the odds of an outcome. Because outcomes are influenced by many factors, we commonly use multivariable logistic regression to estimate the unique influence of each factor. From this tutorial, one should acquire a clearer understanding of how to perform and assess multivariable logistic regression.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".