Randomized Controlled Trials: Do They Have External Validity for Patients With Multiple Comorbidities?
Bibliographic record
Abstract
PURPOSE: Many randomized controlled trials (RCTs) exclude patients who have multiple comorbidities. The aim of this study was to illustrate the prevalence of comorbidities among patients followed up in primary care who would have met the inclusion criteria of selected RCTs focusing on treatment of a particular condition. We used hypertension as an example of a particular chronic condition. METHODS: We used an existing database of 980 patients (660 women) that was representative of a population consulting primary care family doctors and that contained information about all chronic conditions. We randomly selected 5 RCTs that focused on patients with hypertension. The inclusion and exclusion criteria used in each of the 5 RCTs were applied (1 study at a time) to the patients in our database. The patients from our data set who met the inclusion criteria of a given RCT were considered eligible for that RCT. RESULTS: Of the patients from our data set who were eligible for each of the RCTs, 89% to 100% had multiple chronic conditions. The mean number of chronic conditions of patients eligible for each RCT ranged from 5.5 +/- 3.3 to 11.7 +/- 5.3. CONCLUSIONS: Results from this study suggest that RCTs targeting a chronic medical condition such as hypertension could find that, in a sample taken from family practice, most eligible patients have comorbid conditions. Whether these patients are sampled or excluded should be reported. Research results intended to be applied in medical practice should take the complex reality of effective treatment of these patients into consideration.
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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 | 0.694 | 0.884 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".