Invasive Pneumococcal Disease: Still Lots to Learn and a Need for Standardized Data Collection Instruments
Bibliographic record
Abstract
Background.Large studies of invasive pneumococcal disease (IPD) are frequently lacking detailed clinical information.Methods.A population-based 15-year study of IPD in Northern Alberta.Results.2435 patients with a mean age of 54.2 years formed the study group. Males outnumbered females and Aboriginal and homeless persons were overrepresented. High rates of smoking, excessive alcohol use, and illicit drug use were seen. Almost all (87%) had a major comorbidity and 15% had functional limitations prior to admission. Bacteremia, pneumonia, and meningitis were the most common major manifestations of IPD. Almost half of the patients had alteration of mental status at the time of admission and 22% required mechanical ventilation. Myocardial infarction, pulmonary embolism, and new onset stroke occurred in 1.7, 1.3, and 1.1% of the patients, respectively; of those who had echocardiograms, 35% had impaired ventricular function. The overall in-hospital mortality was 15.6%.Conclusions.IPD remains a serious infection in adults. In addition to immunization, preventative measures need to consider the sociodemographic features more carefully. A standard set of data need to be collected so that comparisons can be made from study to study. Future investigations should target cardiac function and pulmonary embolism prevention in this population.
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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 | 0.336 | 0.300 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".