Comparing Effects of Treatment: Controlling for Confounding
Bibliographic record
Abstract
BACKGROUND: Determining true causal links between an intervention and an outcome forms an imperative task in research studies in neurosurgery. Although the study results sometimes demonstrate clear statistical associations, it is important to ensure that this represents a true causal link. A confounding variable, or confounder, affects the association between a potential predictor and an outcome. OBJECTIVE: To discuss what confounding is and the means by which it can be eliminated or controlled. METHODS: We identified neurosurgical research studies demonstrating the principles of eliminating confounding by means of study design and data analysis. RESULTS: In this report, we outline the role of confounding in neurosurgical studies after giving an overview of its identification. We report on the definition of confounding and effect modification, and the differences in the 2. We explain study design techniques to eliminate confounding, including simple, block, stratified, and minimization randomization, along with restriction of sample and matching. Data analysis techniques of eliminating confounding include regression analysis, propensity scoring, and subgroup analysis. CONCLUSION: Understanding confounding is important for conducting a good research study. Study design techniques provide the best way to control for confounders, but when not possible to alter study design, data analysis techniques can also provide an effective control.
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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 | 0.033 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".