Shifting Trends in the Epidemiology of Cervical Spine Injuries: An Analysis of 11,822 Patients from the National Electronic Injury Surveillance System over Two Decades
Notice bibliographique
Résumé
Cervical spine injuries (CSIs) are heterogeneous in nature and often lead to long-term disability and morbidity. However, there are few recent and comprehensive epidemiological studies on CSI. The objective of this study was to characterize recent trends in CSI patient demographics, incidence, etiology, and injury level. The National Electronic Injury Surveillance System was used to extract data on CSIs from 2002 to 2022. Weighted national estimates of CSI incidence were computed using yearly population estimates interpolated from U.S. census data. Data analysis involved extracting additional information from patient narratives to categorize injury etiology (i.e., fall) and identify CSI level. K-means clustering was performed on cervical levels to define upper versus lower cervical injuries. Appropriate summary statistics including mean with 95% confidence intervals and frequency were reported for age, sex, race, ethnicity, etiology, and disposition. Age between groups was compared using an independent weighted Z -test. All categorical variables were compared using Pearson chi-squared tests with Bonferroni correction for multiple comparisons. Ordinary least squares linear regression was used to quantify the rate of change of various metrics with time. A total of 11,822 patient records met the study criteria. The mean age of patients was 62.4 ± 22.7 years, 52.4% of whom were male and 61.4% of whom were White, 7.4% were Black, 27.8% were not specified, and the remaining comprised a variety of ethnicities. The most common mechanism of CSI was a fall (67.3%). There was a significant increase in the incidence of cervical injuries between 2003 and 2022 ( p < 0.001). Unbiased K-means clustering defined upper cervical injuries as C1–C3 and lower cervical injuries as C4–C7. The mean age of patients with upper CSIs was 72.3 ± 19.6, significantly greater than the age of those with lower CSIs (57.1 ± 23.1, p < 0.001). Compared with lower CSI, White patients were more likely to have an upper CSI (67.4% vs. 73.7%; p < 0.001). While Black/African American (7.5% vs. 3.8%) and Hispanic (2.5% vs. 1.0%) patients were more likely to have a lower CSI ( p < 0.001). Our study identified a significant increase in the incidence of CSIs over time, which was associated with increasing patient age. Our study detected a pragmatic demarcation of classifying upper injuries as C1–C3 and lower cervical injuries as C4–C7. Upper injuries were seen more often in older, White females who were treated and admitted, and lower injuries were seen more often in young, Black male patients who were released without admission.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».