TRAFFIC CAMERAS: AN EFFECTIVE AND SUSTAINABLE METHOD OF REDUCING TRAFFIC AND AIRBORNE PARTICLES DURING ARTHROPLASTY SURGERY
Notice bibliographique
Résumé
Prosthetic joint infections (PJI) are now the most common cause of reoperation following hip replacement surgery. Traffic in the operating room (OR) create turbulence and contaminates ultraclean air by bacterial shedding. Therefore, controlling traffic in the OR is an important strategy to prevent infection by reducing airborne particles capable of carrying bacteria. In this study, we examined (1) if the number and duration of door openings was associated with increased particle counts during arthroplasty surgery (2) if a traffic camera installed in the operating room was an effective intervention to decrease traffic and particle counts during arthroplasty surgery and (3) the effectiveness of the traffic camera over time. A prospective observational study examined all primary joint replacements at a high-volume, academic hospital between November 2021 and June 2022. Two aerosolized particle counters were used to count particles sized 0.5–10 μm, corresponding to the particles sizes that most commonly contain bacteria. One particle counting machine was positioned adjacent to the nursing back table within the sterile operative field, and the other was placed between the two OR doors that are used for personnel to enter and leave the OR during surgery. Additionally, two magnetic door counters were mounted on the two operating room doors and were used to count the number of door openings during surgery. For the intervention, we installed warning signs on the doors and traffic cameras facing each door, that took a snapshot with every door opening during surgery. A total of 50 cases were included in the study with 25 cases in the Control group and 25 cases in the Intervention group. The number of door openings/minute was 30% less in the Intervention group than in the Control group (0.7±0.2 vs 1.0±0.3, P < 0 .001). For all particle sizes, compared to the Control group, the Intervention group significantly decreased the particle counts by 26–43% in the operative field (0.5μm, p = 0.01; 0.7μm, p = 0.008; 1μm, p = 0.007; 2.5 μm, p = 0.006; 5um, p = 0.01and 10μm, p = 0.01). The particle counts between the OR doors were decreased by 2–42% in the Intervention group compared to the Control group, and the difference was significant for particles sized 0.5μm, 0.7μm, and 1μm (0.5μm, p=0.03, 0.7μm, p=0.02 and 1μm, p=0.03). A significant decrease (56–78%) was found in the average particle count in the Intervention group between the first 15 minutes and the last 15 minutes of the case compared to the Control group (0.5μm, p=0.01; 0.7μm, p=0.004; 1μm, p=0.002; 2.5μm, p=0.001; 5μm, p=0.004 and 10μm, p=0.01). The decrease in door openings and particle counts attained in the Intervention group were sustained over the entire study period. This study demonstrated that the use of traffic cameras was an effective and sustainable method to limit OR traffic during arthroplasty surgery. The use of door posters and traffic cameras significantly limited OR traffic and reduced door openings, which resulted in a sustained reduction in particle counts near the OR doors, and more importantly within the operative field.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».