Hospital admission for community-acquired pneumonia in a First Nations population.
Bibliographic record
Abstract
INTRODUCTION: Northwestern Ontario is a large rural area with a high concentration of remote First Nations communities. In Ontario, the highest hospital admission rates for pneumonia are reported from northern and rural regions. However, data are lacking on the epidemiology of community-acquired pneumonia in northwestern Ontario. We sought to characterize cases of community-acquired pneumonia requiring admission at the Sioux Lookout Meno Ya Win Health Centre, which serves a primarily First Nations population of 28,000. METHODS: We undertook a 3-year review of cases of community-acquired pneumonia requiring hospital admission at the centre. We used multivariable logistic regression to identify independent variables predictive of adverse outcomes. RESULTS: The annual incidence of hospital admissions related to community-acquired pneumonia was 3.42 per 1000 population. Of the 287 patients, 87% were First Nations and 52% were female. There was a high prevalence of diabetes, and chronic cardiovascular, renal and pulmonary diseases. Hospital admissions for community-acquired pneumonia were most prevalent among young children and older adults; both age groups had low coverage with recommended pneumococcal vaccines. Adverse outcomes included 10 deaths (3%) and 35 transfers to an intensive care facility (12%). Chronic renal disease and nonreceipt of azithromycin at initial presentation were identified as 2 independent predictors of an adverse outcome; there was a trend toward an increased risk of an adverse outcome in individuals with chronic obstructive pulmonary disease. CONCLUSION: Our findings emphasize the importance of preventing pneumonia in First Nations communities in northwestern Ontario. Research focusing on the distinct epidemiology of community-acquired pneumonia in this population is needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".