Bias in Before–After Studies: Narrative Overview for Anesthesiologists
Bibliographic record
Abstract
Before-after study designs are effective research tools and in some cases, have changed practice. These designs, however, are inherently susceptible to bias (ie, systematic errors) that are sometimes subtle but can invalidate their conclusions. This overview provides examples of before-after studies relevant to anesthesiologists to illustrate potential sources of bias, including selection/assignment, history, regression to the mean, test-retest, maturation, observer, retrospective, Hawthorne, instrumentation, attrition, and reporting/publication bias. Mitigating strategies include using a control group, blinding, matching before and after cohorts, minimizing the time lag between cohorts, using prospective data collection with consistent measuring/reporting criteria, time series data collection, and/or alternative study designs, when possible. Improved reporting with enforcement of the Enhancing Quality and Transparency of Health Research (EQUATOR) checklists will serve to increase transparency and aid in interpretation. By highlighting the potential types of bias and strategies to improve transparency and mitigate flaws, this overview aims to better equip anesthesiologists in designing and/or critically appraising before-after studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".