Dificultades en la interpretación de los resultados de la investigación biomédica relacionada con el manejo de pacientes con enfermedades crónicas no transmisibles
Bibliographic record
Abstract
The progressive increase in the prevalence of chronic non-communicable diseases (CNCD) has generated a need to change the paradigms in interpreting research about therapeutic and disease control strategies. One aspect to keep in mind is the incorporation of risk awareness that CNCD treatment implies, which creates uncertainty in the treatment result, compared to the curative paradigm that occurs in communicable diseases where a cure is expected. Another aspect is related to clinical trials result reports, where substitute results are used frequently. For example, the therapeutic goal of reducing glycosylated hemoglobin in a diabetic patient instead of showing the results based on treatment benefit (such as prevention of myocardial infarction). Problems arise when looking for a substitute that can replace the result that really matters. That is why we must be alert to the widespread use of results grouping (composite outcomes) which while they allow studies with fewer patients with shorter follow-up times and less expense, they can generate misleading results and show presumed untrue benefits due to improper selection of components of the "composite outcomes". In this article we draw attention to new challenges in the interpretation of scientific studies related to CNCDs.
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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 | 0.569 | 0.840 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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; 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".