Creation of a Case Definition to Identify People Living with HIV using Canadian Primary Care Sentinel Surveillance Network data
Bibliographic record
Abstract
Abstract Background: HIV care that has been predominantly specialty based can often be managed in primary care settings. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database is a rich source of electronic medical records of primary care practices. A case definition for HIV using CPCSSN allows the examination of epidemiology and healthcare utilization of people living with HIV (PLHIV) whose records are in this database and assists us to better understand the primary care of PLHIV. The objective of this study was to develop a case definition for HIV applicable to the CPCSSN database. Methods: Electronic medical record data from CPCSSN was used to develop a retrospective cohort between 2009 and 2017. We identified all possible records of PLHIV in the CPCSSN dataset based on the presence of HIV codes, keywords and medications. Multiple combination of codes, keywords and medications were analyzed to see which resulted in the most accurate definition of PLHIV. To assess the validity, we linked the data to external references (a Newfoundland and Labrador (NL) PLHIV cohort and Nine Circles HIV clinic in Manitoba) and an internal reference (a random sample of the CPCSSN database which was reviewed by two experts to confirm HIV status). Results: We found that the presence of an HIV keyword along with either an ICD code or taking 3 or more HIV medications concurrently was the most accurate algorithm for predicting PLHIV with a sensitivity of 95%, 97.9% and 88.9%, specificity of 63.5%, 100% and 100% for the NL PLHIV cohort, Nine Circles HIV clinic data and internal reference, respectively. Conclusion: To our knowledge, this is the first study to develop an algorithm for identifying people with HIV applicable to the Canadian Primary Care Sentinel Surveillance Network database. This algorithm-based case definition will support future research investigating the utilization of primary healthcare by PLHIV and facilitate improvements in primary care for PLHIV.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".