Rethinking future workforce planning by developing novel metrics of complexity in cancer care.
Notice bibliographique
Résumé
9015 Background: The decision to recruit additional oncologists is often based on simple workload measures such as new patient volumes. This approach may be appropriate previously when cancer management was less complex and at a time when attrition from cancer mortality was significant. We hypothesized that cancer care complexity has increased over time and that new metrics are needed to optimize workforce planning. Methods: We conducted a population-based, retrospective cohort study of adult patients diagnosed with common solid and blood cancers in Alberta, Canada. We focused on cases from 2004 to 2018 to ensure adequate follow-up. We evaluated indicators of complexity including patient characteristics at the time of diagnosis, clinical course within 2 years of diagnosis, and longevity as measured by overall survival (OS). For these complexity indicators, we used logistic and Cox regression models to estimate relative changes over the 15-year study period. Results: A total of 141,040 patients were included in the study cohort, with a median age of 66 years (range 18-107) and 51.7% male. Breast cancer was most common (25.6%), followed by prostate (24.0%), lung (20.2%), colorectal (17.9%) and leukemia/lymphoma (12.4%). Across all sites, annual cancer incidence rate was 249.9 per 100,000 in 2004 and 284.4 per 100,000 in 2018. Age distribution remained largely stable throughout the study period. Meanwhile, specific indicators of complexity increased over time, including polypharmacy at diagnosis (odds ratio [OR] 1.30, 95% confidence interval [CI] 1.28-1.32), multimodality treatment (OR 1.06, 95% CI 1.05-1.08), and hospital admission via the emergency department (OR 1.08, 95% CI 1.07-1.10). These metrics also demonstrated increasing complexity in multivariable analyses after adjusting for age, sex, and cancer site. Similarly, there was a trend towards greater longevity as measured by OS (hazard ratio 0.98, 95% CI 0.98-0.98). Conclusions: Cancer care complexity has increased over time. Workforce planning using antiquated workload metrics, such as incident patient volumes alone, may not align with the actual demands of providing increasingly complex cancer care. Recruitment strategies should consider multi-faceted indicators that reflect complexity in addition to quantity. Trends in metrics of patient complexity, by time era. Characteristic Year of diagnosis 2004-2008, n = 39,465 2009-2013 , n = 46,408 2014-2018 , n = 55,167 Age, y (range) 66 (18, 104) 66 (18, 104) 66 (18, 107) Stage III-IV a 12,706 (41.4%) 15,265 (39.2%) 17,258 (38.1%) Polypharmacy 6,853 (17.4%) 11,175 (24.1%) 14,836 (26.9%) Multimodality treatment 13,884 (35.2%) 17,069 (36.8%) 21,025 (38.1%) Any admission via ED b 7,679 (19.5%) 9,341 (20.1%) 12,057 (21.9%) 2-year OS (95% CI) 0.70 (0.69-0.70) 0.73 (0.73-0.74) 0.76 (0.75-0.76) a solid cancers only; b within 2 years following diagnosis.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».