Gain in Quality-adjusted Life-years in Patients with Rheumatoid Arthritis During 1 Year of Biological Therapy: A Prospective Study in Clinical Practice
Bibliographic record
Abstract
<b>Objective:</b> To assess the safety of government versus non-government public road transportation. <b>Design:</b> Descriptive study. <b>Setting:</b> Kandy Municipality Area, Sri Lanka. <b>Subjects:</b> All road traffic crashes reported to the Kandy Police from 1 October 1998 to 30 September 1999. <b>Main outcome:</b> Involvement in a road traffic crash reported to the Kandy Police in which a government bus, private bus, or a three-wheeler was involved. <b>Results:</b> During the study period, 132 government buses, 243 private buses, and 115 three wheelers were involved in 437 police reported road crashes. Of these crashes, eight (1.8%) were fatal and 132 (30.2%) were crashes resulting in injury requiring hospitalization. The majority of road crashes involved vehicle-vehicle interaction (63.4%) and vehicle-pedestrian interaction (17.8%), while the remainder consisted of vehicle-passenger and vehicle-road structure crashes. The research highlights an increased risk associated with travel on privately owned buses (RR = 2.0, 95% CI 1.6 to 2.5) and three wheelers (RR = 2.2, 95% CI 1.7 to 2.8) compared to travel on government buses. The disparity in crash rates between government and privately owned transportation modes can be explained, in part, by fewer safety requirements being imposed on the deregulated public transportation system. <b>Conclusions:</b> Recommendations are made in order to address the differential in crash rates between public and private vehicle ownership used for public transportation in Sri Lanka.
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 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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".