3 research outputs found
The Genets from the Island of Ibiza (Genetta genetta isabelae n. ssp.)
No full textLas Ginetas de Ibiza habían sido consideradas hasta ahora como G. g. balearica. El estudio de 50 cráneos y 38 pieles (Cuadro 1) permite, sin embargo, caracterizarlas como una nueva subespecie, Genetta genetta isabelae n. ssp., debido fundamentalmente a su pequeño tamaño. Se consideran caracteres con valor diagnóstico LCB (longitud cóndilo-basal) ≤85 mm. LM (longitud de la mandíbula) ≤60 mm. LPm⁴ (longitud de la carnicera superior) ≤8mm. M² (segundo molar superior) muy reducido. Las manchas del pelaje son negras o marrones oscuras con abundantes pelos de color rojizo leonado. El dimorfismo sexual es poco marcado (Fig. 3). Las medidas de G. g. isabelae (Cuadros 2 y 3) difieren significativamente de las restantes poblaciones del oeste de Europa y norte de África. Estas muestran por su parte una variación clinal de tamaño, desde Francia, donde son más pequeñas, al norte de África donde son más mayores, a través de la Península Ibérica y Mallorca (Cuadros 4 y 5; Fig. 4, 5, 6 y 7). Las Ginetas fueron muy perseguidas en Ibiza, pero aún son comunes en gran parte de la isla, frecuentando los pinares de Pinus halepensis. Su dieta está basada en los micromamíferos, pero incluye hasta un 30% de reptiles, con lo cual es la población paleártica más herpetófaga entre las conocidas. De acuerdo con los escasos datos de que disponemos se reproducen generalmente en primavera y con menos frecuencia en otoño, siendo el tamaño habitual de camada 2. Las ginetas han debido ser introducidas en Ibiza desde la Península Ibérica o desde África. En este sentido, llama poderosamente la atención su similitud con G. (g). senegalensis y G. g. granti. La reducción del tamaño de isabelae está ligada sin duda al carácter insular de su área de distribución, y probablemente a la escasez de carnívoros salvajes en la isla (sólo otra especie, Martes fuina) y a la dieta herperófaga.The genets of Ibiza have just to day been called G. g. balearica. The study of 50 skulls and 38 skins (Table 1) shows them to be a new subspecies,Genetta genetta isabelae n. ssp., due mainly to their small size. Characteristics with diagnostic value are LCB (condylobasal length) ≤85 mm.; LM (Mandibular length) ≤60 mm.; LPm⁴(Pm⁴ lesgth) ≤8mm. asd M² very reduced. The spots on the fur are black or dark brow with abundant hairs of a reddish fulvous colour. Sexual dimorphism is not very noticeable (Fig. 3). The measurements of G. g. isabelae (Tables 2 and 3) differ statistically from those of the other populations of Western Europe and North Africa. These show a clinal variatica in sile, from France, where they are smallest, to the north of Africa, where yhey are largest, trough the Iberian Peninsula and Mallorca (Tables 4 y 5; Fig. 4, 5, 6 y 7). Geners were greatly persecuted in Ibiza but are still quite common in much of the island, frequenting the pine forests of Pinus halepensis. Their diet is composed principally of small mammals, but about 30% of it is made up of by reptiles, which fact makes them the most herpetophagous of the known palearctic populations. In accordance with what little is known, they reproduce generally in spring and less often in the autumn. The usual litter size is two kittens. Genets were probably introduced into Ibiza from the ¡berian Peninsula or from Africa. Their marked similarity to G. (g.) senegalensis and G. g. granti calls attention to itself. The reduced size of isabelae is without doubt linked to the insular nature of its area of distribution and probably to the scarcity of wild carnivores on the island (only one other species, Martes foina and to the herpetophagical diet
Development and external validation of the 'Global Surgical-Site Infection' (GloSSI) predictive model in adult patients undergoing gastrointestinal surgery
No full textBackground
Identification of patients at high risk of surgical-site infections may allow surgeons to minimize associated morbidity. However, there are significant concerns regarding the methodological quality and transportability of models previously developed. The aim of this study was to develop a novel score to predict 30-day surgical-site infection risk after gastrointestinal surgery across a global context and externally validate against existing models.
Methods
This was a secondary analysis of two prospective international cohort studies: GlobalSurg-1 (July–November 2014) and GlobalSurg-2 (January–July 2016). Consecutive adults undergoing gastrointestinal surgery were eligible. Model development was performed using GlobalSurg-2 data, with novel and previous scores externally validated using GlobalSurg-1 data. The primary outcome was 30-day surgical-site infections, with two predictive techniques explored: penalized regression (least absolute shrinkage and selection operator (‘LASSO’)) and machine learning (extreme gradient boosting (‘XGBoost’)). Final model selection was based on prognostic accuracy and clinical utility.
Results
There were 14 019 patients (surgical-site infections = 12.3%) for derivation and 8464 patients (surgical-site infections = 11.4%) for external validation. The LASSO model was selected due to similar discrimination to extreme gradient boosting (AUC 0.738 (95% c.i. 0.725 to 0.750) versus 0.737 (95% c.i. 0.709 to 0.765)), but greater explainability. The final score included six variables: country income, ASA grade, diabetes, and operative contamination, approach, and duration. Model performance remained good on external validation (AUC 0.730 (95% c.i. 0.715 to 0.744); calibration intercept −0.098 and slope 1.008) and demonstrated superior performance to the external validation of all previous models.
Conclusion
The ‘Global Surgical-Site Infection’ score allows accurate prediction of the risk of surgical-site infections with six simple variables that are routinely available at the time of surgery across global settings. This can inform the use of intraoperative and postoperative interventions to modify the risk of surgical-site infections and minimize associated harm
Development and external validation of the ‘Global Surgical-Site Infection’ (GloSSI) predictive model in adult patients undergoing gastrointestinal surgery
No full textBackground: Identification of patients at high risk of surgical-site infections may allow surgeons to minimize associated morbidity. However, there are significant concerns regarding the methodological quality and transportability of models previously developed. The aim of this study was to develop a novel score to predict 30-day surgical-site infection risk after gastrointestinal surgery across a global context and externally validate against existing models. Methods: This was a secondary analysis of two prospective international cohort studies: GlobalSurg-1 (July-November 2014) and GlobalSurg-2 (January-July 2016). Consecutive adults undergoing gastrointestinal surgery were eligible. Model development was performed using GlobalSurg-2 data, with novel and previous scores externally validated using GlobalSurg-1 data. The primary outcome was 30-day surgical-site infections, with two predictive techniques explored: penalized regression (least absolute shrinkage and selection operator ('LASSO')) and machine learning (extreme gradient boosting ('XGBoost')). Final model selection was based on prognostic accuracy and clinical utility. Results: There were 14 019 patients (surgical-site infections = 12.3%) for derivation and 8464 patients (surgical-site infections = 11.4%) for external validation. The LASSO model was selected due to similar discrimination to extreme gradient boosting (AUC 0.738 (95% c.i. 0.725 to 0.750) versus 0.737 (95% c.i. 0.709 to 0.765)), but greater explainability. The final score included six variables: country income, ASA grade, diabetes, and operative contamination, approach, and duration. Model performance remained good on external validation (AUC 0.730 (95% c.i. 0.715 to 0.744); calibration intercept -0.098 and slope 1.008) and demonstrated superior performance to the external validation of all previous models. Conclusion: The 'Global Surgical-Site Infection' score allows accurate prediction of the risk of surgical-site infections with six simple variables that are routinely available at the time of surgery across global settings. This can inform the use of intraoperative and postoperative interventions to modify the risk of surgical-site infections and minimize associated harm
