Differences in prevalence of pre-existing morbility between injured and non-injured populations
Bibliographic record
Abstract
Objectives To identify and examine differences in pre-existing morbidity between injured and non-injured population-based cohorts. Methods Administrative health data from Manitoba, Canada, were used to select a population-based cohort of injured people and a sample of non-injured people matched on age, gender, aboriginal status and geographical location of residence at the date of injury. All individuals aged 18-64 years who had been hospitalized between 1988 and 1991 for injury (International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) code 800-995) (n = 21032), were identified from the Manitoba discharge database. The matched non-injured comparison group comprised individuals randomly selected 1: 1 from the Manitoba population registry. Morbidity data for the 12 months prior to the date of the injury were obtained by linking the two cohorts with all hospital discharge records and physician claims. Results Compared to the non-injured group, injured people had higher Charlson Comorbidity Index scores, 1.9 times higher rates of hospital admissions and 1.7 times higher rates of physician claims in the year prior to the injury. Injured people had a rate of admissions to hospital for a mental health disorder 9.3 times higher, and physician claims for a mental health disorder 3.5 times higher, than that of non-injured people. These differences were all statistically significant (P < 0.001). Conclusion Injured people were shown to differ from the general non-injured population in terms of pre-existing morbidity. Existing population estimates of the attributable burden of injury that are obtained by extrapolating from observed outcomes in samples of injured cases may overestimate the magnitude of the problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".