Capturing factors associated with frailty using routinely collected electronic medical record data in British Columbia, Canada.
Bibliographic record
Abstract
Background: Frailty is a state of increased vulnerability from physical, social, and cognitive factors resulting in greater risk of negative health outcomes. There is potential for better frailty assessment in primary care by using electronic medical record (EMR) data. Aim. To adapt the validated UK 36-item electronic frailty index (eFI) to a Canadian context. Methods. The eFI calculates frailty scores using EMR data. Clinical terminology mapping was required to translate the clinical codes that reflect frailty in the UK eFI to Canadian primary care terminologies (ICD, LOINC, ATC). Manual and automatic mapping was used to develop a superset of codes. We used data from the BC Canadian Primary Care Sentinel Surveillance Network to develop a list of free text terms by searching free text fields related to diagnoses and reasons for patient visits within a sample of patients (65 years) EMRs from July 207 to June 2022. Results: A total of 3768 terms were identified for the frailty factors (302 codes and 747 free text terms). 69% of the factors were captured mostly by codes; 20% mostly by free text; and % were captured approximately equally. Conclusion & Implications: It is difficult to capture frailty using only standardized terminologies used in Canada. A combination of standardized codes and free text better captures the complexity of frailty. This study allows for the development of a frailty screening algorithm and subsequently a frailty screening tool that can be implemented in primary care frailty screening, resulting in improved patient and system level outcomes.Funding sources: Canadian Institutes of Health Research, Canadian Nurses Foundation.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".