The landscape of prostate cancer research in Canada.
Notice bibliographique
Résumé
394 Background: Prostate cancer (PCa) is the most prevalent malignancy in Canadian men; in 2023, an estimated 24,700 men will be diagnosed while ~4,700 will die of their disease. Lifetime risk of developing PCa is approximately 1-in-8 and approximately 3% of all deaths of Canadian men are caused by the disease. While Canada has a robust, modern health research landscape, with major academic centres located in all provinces and territories, the investment in academic PCa research – and the impact of that investment – has not been systematically quantified. An improved understanding of this landscape is necessary to ensure research funding remains efficient, impactful, and equitable. As such, we evaluated the size, scope, and impact of the investment in academic PCa research in Canada. Methods: We extracted funding records from the Canadian Research Information System (CRIS), the United States Congressionally Directed Medical Research Program database and the United States National Institutes of Health RePORT database for the National Cancer Institute, using keywords ‘prostate’ OR ‘prostatic’ AND ‘cancer’ OR ‘carcinoma’ for 1999-2021. US-sourced records were included when the principal investigator (PI) was based at a Canadian institution at the time the award was granted. Records were validated using data from the Canadian Cancer Research Alliance (CCRA). Intramural and industry-derived funding was unavailable and thus excluded from the analysis. Results: We identified 1,748 unique funding events (FEs) from 33 sources. These FEs involved 1,561 investigators and had an inflation-adjusted value of $682,113,116. The top three funders of PCa research were the Canadian Institutes of Health Research, Movember Canada, and the Canadian Cancer Society. Basic and translational research received ~83% of all funding while psychosocial, health economics, and epidemiology research received only ~7.2%. Strikingly, we found that 30.5% of all funding was held by 1% of investigators. We identified 6,671 dyads ( ie. pairs of collaborating investigators); 85% collaborated only once, while 15% collaborated at least twice (range: 2-19). Female investigators participated in significantly fewer collaborations than males (P: 3.28 x 10-3) and were less likely than expected to serve as PI (P: 8.87 x 10-8). FEs with ≥ one female PI had a significantly lower value than those with only male PIs (P: 1.34 x 10-6) and FEs with only female PIs had a significantly value than those with only male PIs (P: 7.32 x 10-4). Conclusions: While Canadian PCa research has been highly funded over the past 25 years, there remain substantial funding disparities across scientific disciplines, geographic regions, and, in particular, gender. There is also substantial ‘wealth inequality’; a small minority of investigators receive most of the funding. We are currently assessing stakeholder attitudes toward these disparities, to help inform the next phase of PCa research funding in Canada.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,016 | 0,048 |
| Études des sciences et des technologies | 0,008 | 0,002 |
| Communication savante | 0,008 | 0,001 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».