Development and Validation of a Brief Instrument to Evaluate Primary-Care AMI Management in Mexico
Bibliographic record
Abstract
This descriptive cross-sectional study developed and validated an instrument to evaluate the initial management of acute myocardial infarction (AMI) at the primary-care level in Mexico. The instrument was constructed from the Mexican Social Security Institute Infarction Code and the national Clinical Practice Guideline, extracting core elements for first-contact AMI care. Expert judgment guided item selection using the Rovinelli and Hambleton approach, and items with Aiken’s index ≥0.70 were retained. A pilot test with 35 primary-care physicians assessed the preliminary version. The field sample comprised 143 physicians from the 17 municipalities of Tabasco, selected by convenience sampling. Reliability was estimated with Cronbach’s alpha. The pilot version showed α=0.636; after expert validation and refinement—including the addition of two items (on fibrinolytic dosing in adults ≥75 years and post-fibrinolysis protocol)—the final 10-item instrument achieved α=0.817. Corrected item–total correlations improved notably for item 2 (from 0.243 to 0.544), while items 5 and 8 showed the highest values in the final version. Factorability was adequate (KMO = 0.736; Bartlett’s χ²(36) = 83.609, p < 0.001). This brief, context-specific tool shows solid internal consistency and expert-supported content validity for primary-care AMI management; structural and criterion (predictive) validity should be further confirmed.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".