Birth Month and Cardiovascular Disease Risk Association: Is meaningfulness in the eye of the beholder?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the modern era, with high-throughput technology and large data size, associational studies are actively being generated. Some have statistical and clinical validity and utility, or at least have biologically plausible relationships, while others may not. Recently, the potential effect of birth month on lifetime disease risks has been studied in a phenome-wide model. We evaluated the associations between birth month and 5 cardiovascular disease-related outcomes in an independent registry of 8,346 patients from Ontario, Canada in 1977-2014. We used descriptive statistics and logistic regression, along with model-fit and discrimination statistics. Hypertension and coronary heart disease (of primary interest) were most prevalent in those who were born in January and April, respectively, as observed in the previous study. Other outcomes showed weak or opposite associations. Ancillary analyses (based on raw blood pressures and subgroup analyses by sex) demonstrated inconsistent patterns and high randomness. Our study was based on a high risk population and could not provide scientific explanations. As scientific values and clinical implications can be different, readers are encouraged to read the original and our papers together for more objective interpretations of the potential impact of birth month on individual and public health as well as toward cumulative/total evidence in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it