Coronary milking-like effect caused by systolic expansion of a traumatic focal left ventricular aneurysm
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
<h3>Background</h3> People with disability, multiple chronic conditions or both may experience challenges in accessing primary care. We aimed to determine the association between appropriate cervical cancer screening and level of disability among women eligible for screening in Ontario and the influence of relevant sociodemographic and health-related variables, including level of morbidity (measured by number of chronic conditions), on screening. <h3>Methods</h3> We used multiple linked databases, including 2 waves of the Canadian Community Health Survey (2005 and 2007/08). Of the 22 824 women included in the study, 7600 reported some level of disability. We used Ontario Health Insurance Plan fee codes to identify appropriate cervical cancer screening. <h3>Results</h3> Compared with women without disability, women with disability were older, less educated, had lower income and had more chronic conditions (36.2% had at least 2 conditions v. 8.4% of women without disability). Women with no disability and no chronic conditions were more frequently screened appropriately than those with severe disability and 2 or more chronic conditions (64.5% v. 39.8%). In multivariable logistic regression analysis, age, rurality, education, marital status and household income were each independently associated with cervical cancer screening. There was a significant interaction between level of morbidity and level of disability. Women with a higher level of disability were less likely to be screened than women with lower level of disability as their level of morbidity increased. <h3>Conclusion</h3> The rate of screening for cervical cancer is low among women with both disability and multimorbidity. Policymakers should note these results as they work toward improving cancer screening rates for an aging population with complex medical needs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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