Bibliographic record
Abstract
Importance: Although nasal bones are the most common type of facial fracture given their natural projection and vulnerability to trauma, there is a paucity of data on its trends. Objective: To report on the trends and costs associated with open and closed nasal bone fractures across the United States. Methods: A retrospective analysis from 2006 to 2014 was conducted of the Nationwide Emergency Department Sample by using the International Classification of Disease, Ninth Revision codes for closed and open nasal bone fractures (802.0 and 802.1) presenting to emergency departments (ED). Trend analysis of total number and rate of visits, discharges, admissions, and associated costs were conducted. Results: Data from 1,253,399.741 records were collected. The total number of ED visits decreased by 2.05% for both open and closed nasal fractures from 2006 to 2014 whereas their associated costs increased ( p < 0.001 and p < 0.05 for closed and open nasal fractures). Notably, open fractures were consistently costlier whereas closed fractures had a greater percent-increase in costs (76.65%). Conclusions and Relevance: This study identified a significant rise in nasal fracture costs, which can be reduced via use of cheaper diagnostic modalities and cost-effective endoscopic procedures.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".