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Enregistrement W2320270113 · doi:10.1158/0008-5472.sabcs13-p6-06-56

Abstract P6-06-56: Emergency room visits and hospital admission rates after curative chemotherapy for breast cancer. A retrospective single center experience

2013· article· en· W2320270113 sur OpenAlexaffabout
NM Pittman, Mihaela Mates, WM Hopman

Notice bibliographique

RevueCancer Research · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueCancer Diagnosis and Treatment
Établissements canadiensKingston Health Sciences Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineBreast cancerStage (stratigraphy)CancerInternal medicineDyslipidemiaRetrospective cohort studyChemotherapyDisease

Résumé

récupéré en direct d'OpenAlex

Abstract Purpose:Patients undergoing curative (adjuvant or neoadjuvant) chemotherapy (CT) for breast cancer in the Southeast Ontario Local Heath Integration Network (LHIN) have higher rates of emergency room (ER) visits and hospital admissions (HA) compared to other LHINs in Ontario, Canada. This study is to determine factors associated with ER visits and HA after curative chemotherapy for breast cancer in a tertiary Ontario hospital. Methods:A retrospective study was conducted of all patients who completed at least one cycle of curative CT for breast cancer at the Cancer Centre for Southeastern Ontario (CCSEO) in 2011 and 2012. We recorded all ER visits and/or HA within 30 days of any cycle of CT. Data collected included demographics, co-morbidities, type and date of surgery, pathologic tumour characteristics (stage, grade and receptor status), type of CT (adjuvant or neoadjuvant) and number of cycles, use and type of granulocyte-colony stimulating factors (G-CSF). We recorded dates and reasons for ER visits, referring patterns to the ER, date of admission and length of stay. Chi-square and t-tests were calculated to determine factors associated with ER visits and HA. Results:149 patients received curative CT at the CCSEO in 2011 and 2012. Mean age was 58 (range 31-88). 97.3% of patients were female. Comorbidities included hypertension (28.4%), diabetes (10.1%), dyslipidemia (10.1%), coronary artery disease (4.7%) and COPD (2%). Stage distribution was: 4.3% stage 1, 48.6% stage 2 and 36.4% stage 3. 60.8% of patients had grade 3 tumors. 69.8% were ER positive and 61.1% were PR positive. 26.2% were Her2 positive. 62.4% had breast conserving surgery and 56.4% had sentinel lymph node biopsy. Most patients received adjuvant CT (85.2%). The most common CT regimen was FEC-D (89.9%), followed by TC (5.4%) and CMF (4%). 88.6% of patients received G-CSF (either Neupogen or Neulasta) at some point during CT. 53% (n = 79) of patients were seen in the ER at least once within 30 days of CT while 13.4% (n = 20) were admitted to hospital. 36.7% (n = 29) had multiple ER visits. There were a total of 133 ER visits. The most common causes of ER visits were fever without neutropenia (23.3%), pain (13.5%) and febrile neutropenia (9%). Most ER visits occurred on weekdays (74%). We analyzed the following factors associated with ER visits and HA rates: age, gender, comorbidities, TNM staging, grade, receptor status, type of surgery and CT (adjuvant versus neoadjuvant). The only statistically significant factor associated with a higher likelihood of ER visits was stage IIIC breast cancer (p = 0.045). Statistically significant factors associated with HA were tumor size with T2 more likely to be admitted (p = 0.019), adjuvant CT (p = 0.045) and number of CT cycles (p = 0.017). Conclusions:Over half of all patients receiving curative CT for breast cancer at the CCSEO in 2011 and 2012 visited the ER at least once and more than 1/3 required multiple visits. The only factor associated with ER visits included stage of disease. Factors associated with HA were tumour size, adjuvant CT and number of CT cycles. While most patients received G-CSF at some point during their CT, febrile neutropenia was still the third most common reason for ER visits. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-06-56.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,115
Score d'incertitude au seuil0,994

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0070,000

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 Opus0,039
Tête enseignante GPT0,410
Écart entre enseignants0,371 · 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

Citations1
Publié2013
Routes d'admission2
Résumé présentoui

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