First Nations Pneumonia Admissions: Different Patients or Different Attitudes?
Bibliographic record
Abstract
In this issue of the Canadian Respiratory Journal, Marrie et al (pages 336‐342) present a database study of hospital admissions among First Nation Aboriginals (FNAs) in Alberta that is fascinating, at least to me. They captured all hospital admissions for "status" FNAs from 1997 to 1999, along with data on where and how long they were hospitalized, the severity of the pneumonia, the number of comorbidities present, whether they were readmitted and the costs involved. They compared these finding with a group of age‐ and sex‐matched non‐FNAs who were also hospitalized for pneumonia. There are, of course, weaknesses in the study that commonly occur in most exercises using administrative databases. Pneumonia is a hospital record diagnosis (there is no information about chest x‐rays, sputum cultures, etc). Pneumonia severity assessment relies on information regarding hospital transfers, intensive care unit admissions and events such as shock, artificial ventilation and death (there is no information available to apply an accepted grading system) (1). Further, "status" FNAs were probably not entirely representative of FNAs in general; indeed, some nonstatus FNAs may well have been included in the control group. However, I strongly doubt that these or similar objections are substantial enough to greatly influence the findings of Marrie et al.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".