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Record W7001641708

La splenectomia nel cane: indicazioni, urgenze chirurgiche e tempi di sopravvivenza nei pazienti splenectomizzati

2015· article· it· W7001641708 on OpenAlexaboutno aff

Bibliographic record

VenueElectronic Theses and Dissertations Repository (University of Pisa) · 2015
Typearticle
Languageit
FieldMedicine
TopicAbdominal Trauma and Injuries
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)PopulationLimitingWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Obiettivo: determinare nel cane le patologie spleniche più frequenti che richiedono un trattamento chirurgico, le razze predisposte, valutare rischi e benefici della splenectomia, definire i tempi di sopravvivenza nei pazienti splenectomizzati. Animali: In questo studio sono stati inclusi 58 cani di qualsiasi razza ed età che hanno richiesto un intervento di splenectomia. 39 cani sottoposti ad intervento di splenectomia in urgenza, 19 cani sottoposti a chirurgia programmata. Materiali e metodi: è stato confrontato l’andamento perioperatorio nei cani in cui è stata eseguita una splenectomia in urgenza, rispetto ai cani in cui è stata effettuata una splenectomia programmata, considerando segni clinici, condizioni patologiche, complicazioni intra e postoperatorie, giorni di ricovero, diagnosi istologica e tempi di sopravvivenza. Risultati: la rottura di massa splenica con emoperitoneo rappresenta l’indicazione chirurgica più riscontrata. Meticci, Pastori Tedeschi e Labrador sono state le razze più colpite. I pazienti sottoposti a splenectomia in urgenza hanno presentato più complicazioni nel periodo perioperatorio e una mortalità maggiore rispetto i pazienti sottoposti a splenectomia programmata. Neoplasie maligne sono state riscontrate maggiormente nei pazienti operati in urgenza, e l’emangiosarcoma rappresenta la patologia neoplastica di maggiore riscontro. I pazienti sottoposti a splenectomia programmata hanno presentato un tempo medio di sopravvivenza di 715 giorni rispetto i 234 giorni dei pazienti operati in urgenza. Conclusioni: dal confronto si evince come le splenectomie in urgenza si siano mostrate molto svantaggiose rispetto le chirurgie programmate. L’introduzione della medicina preventiva aiuterebbe a prendere in tempo determinate patologie, evitando di intervenire in pazienti critici e consentendo una gestione perioperatoria più sicura, aumentando il tempo di sopravvivenza dei pazienti splenectomizzati. Porpuse: to identify the most common splenic diseases in dogs, according to predisposed dog breeds, evaluate the risks and benefits of splenectomy, define survival times in patients splenectomized. Animals: in this study 58 dogs of variuos age and breed were included.39 dogs required an urgent splenectomy and 19 dogs required splenectomy like programmed surgey. Materials and Methods: the perioperative phases were compared in dogs treatmed with a urgent or programmed splenectomy. in all patients clinical signs, pathological conditions, the intraoperative and postoperative complications, days of hospitalization, histological diagnosis and survival time observed. Results: splenic mass breaking with hemoperitoneum was the surgical indication most frequentely observed. Half-breed, German Shepherd and Labrador Retriever were the most affected breeds. For dogs treated by urgent splenectomy, in postoperative phases complications were observed more frequently than in subjects which were treated by programmed surgery. Maligant neolpasms such as heamngiosarcoma observed frequently in patients which were treated by urgent surgery. Finally, survival time for dogs treated by programmed splenectomy and dogs treated by urgent surgery ,was about 715 and 234 respectively. Conclusion: urgentnsplenectomy is more disadvantageous than programmed surgery. preventive medicine should help to determined splenic diseases, avoiding to intervene in critical patientsand Keywords: dog, splenectomy, hemoperitoneum, hospitalization, complications, programmed surgery, urgent surgery, hemangiosarcoma, survival time.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.248
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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