MétaCan
Menu
Retour à la cohorte
Enregistrement W150623277

The ovulation timing service for bitches at the department of clinical sciences of companion animals at Utrecht University: a retrospective study on the period 2003-2010 and a client satisfaction survey

2011· dissertation· en· W150623277 sur OpenAlexaboutno aff
L.J.C. Leijen

Notice bibliographique

RevueUtrecht University Repository (Utrecht University) · 2011
Typedissertation
Langueen
DomaineVeterinary
ThématiqueVeterinary Medicine and Surgery
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicinePeriod (music)Retrospective cohort studyPsychologySurgeryArt
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Part I
\nThe effects of age, parity, breed, weight and fertilization method on pregnancy rates and litter size were studied in 681 bitches of 124 different breeds. Furthermore, the effect of season on the distribution of presenting dogs for ovulation timing was examined. Data collected from Utrecht University during 2003-2010 were analyzed. The number of dogs presented for ovulation timing was reduced between 2003 and 2008. Pregnancy rates of dogs presented without fertility problems (85.4%) were higher than pregnancy rates of dogs presented with fertility problems (66.5%). In bitches of one (P = 0.037) and two years old (P = 0.048), which gave birth to their first litter, pregnancy rates were higher than those of bitches of seven years old. Labrador retrievers had a significant (P = 0.01) higher pregnancy rate than Golden retrievers (respectively 90.2% and 74.6%). The mean number of total Doberman pups per litter (8.43± 3.52) was higher than the mean number of German shepherd pups (6.17 ± 3.07, P = 0.04). The median weight of the bitches presented for ovulation timing was 29.4 kg, ranging from 2.0 to 71.1. Pregnancy rates were higher in bitches of 10.0-24.9 kg (87.7%) compared to bitches over 45.0 kg (72.7%, P = 0.02). Weight was also found to effect total litter size. Dogs up to 9.9 kg had smaller litters (4.15 ± 1.39, P = 0.005) than dogs between 10.0-24.9 kg (6.49 ± 2.73). Both those groups had smaller litters (respectively P ≤ 0.001 and P ≤ 0.02) than dogs of 25.0-44.9 kg (7.47 ± 2.86) and dogs of more than 45.0 kg (8.40 ± 3.73). A difference (P ≤ 0.001) in pregnancy rate was found between natural matings (85.1%), artificial inseminations with fresh semen (66.7%) and artificial inseminations with frozen-thawed semen (27.8%). Parity did not have an effect on both pregnancy rates and litter size and season did not affect the number of dogs presented for ovulation timing.
\n
\nPart II
\nThe opinion of dog owners concerning the ovulation timing service at Utrecht University (UKG) was examined by an online survey. All clients from the Netherlands (n = 386) between 2003 and 2010, were invited to fill in the questionnaire. The overall response rate was 22.7%. Propositions were rated on a scale of one to five. Veterinarians were evaluated to be capable for examination (mean = 4.6) and the oral information given by them was understandable (mean = 4.4). Co-assistants handled the dogs in a responsible way (mean = 4.2). Least satisfied, were people about the waiting time (mean = 3.8) and reasonableness of the costs (mean = 3.6). Dog owners, who had been to UKG for ovulation timing more than 5 times, were less satisfied about the costs than people who used this service infrequently. 60% of the respondents indicated that they would like to receive additional written information. In general, the ovulation timing service was rated with a median of 8.0 (range 1-10, n = 95) on a scale of 1 to 10.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,059
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0030,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,187
Tête enseignante GPT0,337
Écart entre enseignants0,150 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2011
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueUtrecht University Repository (Utrecht University)Même sujetVeterinary Medicine and SurgeryTravaux en français237 207