Potential for bias in waiting time studies: events between enrolment and admission
Bibliographic record
Abstract
STUDY OBJECTIVE: To demonstrate the effect of exclusion of data on delays in scheduling operations in calculating difference in admission rates between two enrolment periods. DESIGN: A prospective cohort study; outcome measure-waiting time for elective admission; study variables-enrolment periods, before 31 March 1997 and after that date; the time of scheduling delay; gender; age; urgency, and type of surgery. SETTING: An acute care hospital in Ontario, Canada. PARTICIPANTS: 1173 consecutive cases accepted for elective vascular surgery between 1 July 1994 and 31 March 1999. MAIN RESULTS: Before adjustment for scheduling delays, a 20% lower admission rate was associated with period 2, rate ratio (RR) = 0.8 (95% confidence intervals (CI)= 0.7, 0.9). The difference between the periods became only marginally significant after the adjustment, RR = 0.9 (95% CI=0.8, 1.0). No difference between the periods was found when admission rates were compared before a delay occurred, RR = 0.9 (95% CI=0.8, 1.1). In delayed patients, those enrolled in period 1 and 2 had, respectively, a 40% and a 60% lower admission rate than the period 1 patients admitted without scheduling delays, RR = 0.6 (95% CI=0.4, 0.8) for period 1 and RR = 0.4 (95%CI=0.3, 0.5) for period 2. CONCLUSIONS: The results provide evidence that patients experiencing a delay in scheduling operation have a lower admission rate after the event. Thus, potential for bias exists when between group comparison of waiting time is done without adjustment for an intermediate event that may occur before elective admission.
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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 | 0.535 | 0.736 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".