Capturing factors associated with frailty using routinely collected electronic medical record data in British Columbia, Canada, primary care settings
Bibliographic record
Abstract
BACKGROUND: Electronic medical record (EMR) systems in primary care present an opportunity to address frailty, a significant health concern for older adults. Researchers in the UK used Read codes to develop a 36-factor electronic frailty index (eFI), which produces frailty scores for patients in primary care settings. AIM: We aimed to translate the 36-factor eFI to a Canadian context. METHODS: We used manual and automatic mapping to develop a coding set based on standardized terminologies used in Canada to reflect the 36 factors of the eFI. Manual mapping was completed independently by two coders, followed by group consensus among the research team. Automatic mapping was completed using Apelon TermWorks. We then used EMR data from the British Columbia Canadian Primary Care Sentinel Surveillance Network. We searched structured data fields related to diagnoses and reasons for patient visits to develop a list of free text terms associated with any of the 36 factors. RESULTS AND CONCLUSIONS: A total of 3768 terms were identified; 3021 were codes. A total of 747 free text terms were identified from 527,521 reviewed data entries. Of the 36 frailty factors, 24 were captured mostly by codes; 7 mostly by free text; and 4 approximately equally by codes and free text. Three key findings emerged from this study: (1) It is difficult to capture frailty using only standardized terminologies currently used in Canada and a combination of standardized codes and free text terms better captures the complexity of frailty; (2) EMRs in primary care can be better optimized; (3) Output from this study allows for the development of a frailty screening algorithm that could be implemented in primary care settings to improve individual and system level outcomes related to frailty.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".