FY. The impact of influenza-associated respiratory illnesses on hospitalisation, physicians visit, emergency room visit and mortality
Bibliographic record
Abstract
Objectives: Although the increased risk of hospitalization and mortality during influenza seasons has been documented extensively, there is a relative paucity of research on the impact of influenza-related illnesses on other health care use indicators, such as physician use. The purpose of this study was to examine the impact of influenza-associated respiratory illnesses on the Winnipeg health care system, including hospitalizations, physician visits and emergency room visits. Their impact on mortality was also examined. Methods: Administrative data were used to track health care use and mortality over four influenza seasons (1995-96 to 1998-99). Excess health care use and deaths were calculated by subtracting rates during influenza seasons from those during weeks when influenza viruses were not circulating. Results: Significant excess hospitalization, physician visit, and emergency room visit rates emerged for influenza and pneumonia, acute respiratory diseases, and chronic lung disease, especially among children and adults aged 65 and over. Considerable excess mortality due to influenza and pneumonia and chronic lung disease among individuals aged 65 and over also
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".