Representativeness of an HIV cohort of the sites from which it is recruiting: results from the Ontario HIV Treatment Network (OHTN) cohort study
Bibliographic record
Abstract
BACKGROUND: Participation bias is a well-known phenomenon in epidemiologic research, where individuals consenting to research studies differ from individuals who are not able or willing to participate. These dissimilarities may limit the generalizability of results of research studies. Quantification of the participation bias is essential for the interpretation of research findings. METHODS: The Ontario HIV Treatment Network Cohort Study (OCS) is an ongoing open cohort study of HIV positive individuals receiving care at one of 11 sites in Ontario. OCS participants from 4 sites were compared to non-participants (those who declined or were not approached) at those sites with regard to gender, age, HIV risk factor, CD4 count and viral load (VL). Generalized logit regression models were used to identify predictors of declining to participate or not being approached to participate. RESULTS: Compared to participants (P) in the OCS, individuals who declined to participate (D) and those who were not approached (NA) were slightly younger (D:45, NA:44 vs P:46), less likely to be male (D: 71%, NA:75% vs P:88%), less likely to be Caucasian (D:41%, NA:57% vs P:72%) and less likely to be Canadian-born (D: 39%, NA: 52% vs P: 69%). Patients who were not approached to participate were less likely to have VL < 50 copies/mL than other patients (D: 75%, NA: 62%, P: 74%) and had lower CD4 counts than OCS participants (D: 450 cells/mm3, NA: 420 cells/mm3, P: 480 cells/mm3). CONCLUSIONS: Significant demographic and clinical differences were found between OCS participants and non-participants. Extrapolation of research findings to other populations should be undertaken cautiously.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".