Methodological issues associated with using different cut-off points to categorize outcome variables.
Bibliographic record
Abstract
Knowledge of the factors that contribute to delay in seeking medical treatment for acute myocardial infarction (AMI) provides the basis for interventions that are intended to facilitate prompt care-seeking behaviour. However, operational definitions of delay time vary across research studies. The use of inconsistent cut-off times to distinguish between delayers and non-delayers is likely to compromise comparability and generalizability of the findings across studies. The purpose of this paper is to examine the impact of inconsistent operationalization of delay, in terms of cut-off times, on the validity of research findings pertaining to identifying its predictors. Secondary data analysis was performed using a sample of 73 patients who had recently experienced out-of-hospital AMI and concluded that their symptoms were related to the heart. Several regression models were built to examine the influence of using different cut-off times (1, 2, 3, 6, and 12 hours, median delay) on the number and nature of predictors ofAMI care-seeking delay. The impact of varying cut-off times on the explained variance, sensitivity, specificity, and predictive values associated with each regression model was examined. The use of different cut-off times produced different sets of independent predictors, which varied in number and nature. The variance explained by the different regression models as well as their classification indices varied. Use of different cut-off times for the definition of delay time led to inconsistent results. Thus, it is recommended that criteria be established among clinicians and researchers with regard to operationally defining care-seeking delay for AMI.
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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 | 0.581 | 0.691 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".