Cancer recurrence times from a branching process model

As cancer advances, cells often spread from the primary tumor to other parts of the body and form metastases. This is the main cause of cancer related mortality. Here we investigate a conceptually simple model of metastasis formation where metastatic lesions are initiated at a rate which depends on the size of the primary tumor. The evolution of each metastasis is described as an independent branching process. We assume that the primary tumor is resected at a given size and study the earliest time at which any metastasis reaches a minimal detectable size. The parameters of our model are estimated independently for breast, colorectal, headneck, lung and prostate cancers. We use these estimates to compare predictions from our model with values reported in clinical literature. For some cancer types, we find a remarkably wide range of resection sizes such that metastases are very likely to be present, but none of them are detectable. Our model predicts that only very early resections can prevent recurrence, and that small delays in the time of surgery can significantly increase the recurrence probability.Comment: 26 pages, 9 figures, 4 table

A community-sourced glossary of open scholarship terms

Supplementary Information: This list of terms represents the ‘Open Scholarship Glossary 1.0’ (available at: https://forrt.org/glossary/. Glossary available under a CC BY NC SA 4.0 license at: https://static-content.springer.com/esm/art%3A10.1038%2Fs41562-021-01269-4/MediaObjects/41562_2021_1269_MOESM1_ESM.pdf).https://static-content.springer.com/esm/art%3A10.1038%2Fs41562-021-01269-4/MediaObjects/41562_2021_1269_MOESM1_ESM.pd

On the trajectory of discrimination: A meta-analysis and forecasting survey capturing 44 years of field experiments on gender and hiring decisions

A preregistered meta-analysis, including 244 effect sizes from 85 field audits and 361,645 individual job applications, tested for gender bias in hiring practices in female-stereotypical and gender-balanced as well as male-stereotypical jobs from 1976 to 2020. A “red team” of independent experts was recruited to increase the rigor and robustness of our meta-analytic approach. A forecasting survey further examined whether laypeople (n = 499 nationally representative adults) and scientists (n = 312) could predict the results. Forecasters correctly anticipated reductions in discrimination against female candidates over time. However, both scientists and laypeople overestimated the continuation of bias against female candidates. Instead, selection bias in favor of male over female candidates was eliminated and, if anything, slightly reversed in sign starting in 2009 for mixed-gender and male-stereotypical jobs in our sample. Forecasters further failed to anticipate that discrimination against male candidates for stereotypically female jobs would remain stable across the decades.fals

A community-sourced glossary of open scholarship terms

Open scholarship has transformed research, introducing a host of new terms in the lexicon of researchers. The Framework of Open and Reproducible Research Teaching (FORRT) community presents a crowd-sourced glossary of open scholarship terms to facilitate education and effective communication between experts and newcomers

A community-sourced glossary of open scholarship terms

Open scholarship has transformed research, and introduced a host of new terms in the lexicon of researchers. The ‘Framework for Open and Reproducible Research Teaching’ (FORRT) community presents a crowdsourced glossary of open scholarship terms to facilitate education and effective communication between experts and newcomers.</p