Diabetic CVI figures for England and Wales (2007–2009): Table 1
Bibliographic record
Abstract
Abstract Objective To examine mortality rates among HIV-positive indigenous people and others after initiation of HIV care improvements based on the chronic care model to address high HIV-related mortality. Design Retrospective cohort preintervention-to-postintervention evaluation study. Setting Urban-core primary health care centre focused on indigenous people in Vancouver, BC. Participants Individuals infected with HIV. Intervention Adoption of the chronic care model to improve HIV care over time. Main outcome measures All-cause mortality and HIV-related mortality rates, overall and from preintervention (2007 to 2009) to postintervention (2010 to 2012), by indigenous ethnicity, were calculated from clinical data linked with the provincial HIV treatment clinical registry. Results Of the 546 eligible study patients, 323 (59%) self-identified as indigenous. Indigenous persons had higher all-cause mortality compared with other patients (14% vs 8%, P = .035; 6.25 vs 4.02 per 100 person-years [PYRs], P = .113), with an adjusted hazard ratio of 1.77 (95% CI 0.95 to 3.30). Indigenous persons also had higher HIV-related mortality (6% vs 2%, P = .027; 2.50 vs 0.89 per 100 PYRs, P = .063), with an adjusted hazard ratio of 2.88 (95% CI 0.93 to 8.92). Between 2007 to 2009 and 2010 to 2012, a significant decline was observed in all-cause mortality for indigenous patients (10.00 to 5.00 per 100 PYRs, P = .023) and a non-significant decline was observed in other patients (7.21 to 2.97 per 100 PYRs, P = .061). A significant decline in HIV-related mortality was also seen for indigenous patients (5.56 to 1.80 per 100 PYRs, P = .005). Conclusion Despite the overall higher risk of death among indigenous patients compared with others, the decline in mortality in HIV-positive indigenous patients after the initiation of efforts to improve HIV care at the clinic further support HIV primary care informed by indigenous issues and the adoption of the chronic care model.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".