Effect of comorbidities and medications on frequency of primary care visits among older patients.
Bibliographic record
Abstract
OBJECTIVE: To determine if comorbidities and high-risk medications affect the frequency of family physician visits among older patients. DESIGN: Retrospective chart review. SETTING: Academic family health team at Sunnybrook Health Sciences Centre in Toronto, Ont. PARTICIPANTS: Among patients aged 65 years and older who were registered patients of the family health team between July 1, 2013, and June 30, 2014, the 5% who visited their family physicians most frequently and the 5% who visited their family physicians least frequently were selected for the study (N = 265). MAIN OUTCOME MEASURES: Predictors of frequent visits to family physicians. RESULTS: The significant predictors of being a high-frequency user were female sex (odds ratio [OR] = 2.20, P = .03), age older than 85 years (OR = 5.35, P = .001), and higher total number of medications (OR = 1.49, P < .001). Age-adjusted Charlson comorbidity index score, number of Beers criteria medications, and Anticholinergic Risk Scale score were not significant predictors (P > .05). CONCLUSION: Female sex, age older than 85, and higher total number of medications were independent significant predictors of higher frequency of family physician visits among older patients. Validated tools, such as the Charlson comorbidity index, Beers criteria, and Anticholinergic Risk Scale, did not independently predict the frequency of visits, indicating that predicting frequency of visits is likely complex.
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.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".