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Copy file name to clipboardexpand all lines: evaluation/badges.html
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Although this script uses Python, it is applicable regardless of the language used by the study you are evaluating.
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<p>This page evaluates the extent to which Monks et al. 2016 meets the criteria of badges related to reproducibility from various organisations and journals.</p>
<p>TO DO: Add a table (perhaps minimise-able) that summarises the criteria of each badge, so can see how/why did or did not meet each of the badges.</p>
<divclass="sourceCode cell-code" id="cb5"><preclass="sourceCode python code-with-copy"><codeclass="sourceCode python"><spanid="cb5-1"><ahref="#cb5-1" aria-hidden="true" tabindex="-1"></a><spanclass="co"># Identify which badges would be awarded based on criteria</span></span>
<li><ahref="#not-in-scope" id="toc-not-in-scope" class="nav-link" data-scroll-target="#not-in-scope">Not in scope</a></li>
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</ul></li>
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<li><ahref="#key-results-from-elsewhere-in-article" id="toc-key-results-from-elsewhere-in-article" class="nav-link" data-scroll-target="#key-results-from-elsewhere-in-article">Key results from elsewhere in article</a></li>
<divclass="toc-actions"><ul><li><ahref="https://github.com/pythonhealthdatascience/stars-reproduce-allen-2020/edit/main/evaluation/scope.qmd" class="toc-action"><iclass="bi bi-github"></i>Edit this page</a></li><li><ahref="https://github.com/pythonhealthdatascience/stars-reproduce-allen-2020/issues/new" class="toc-action"><iclass="bi empty"></i>Report an issue</a></li></ul></div></nav>
<figcaption>Figure 2. “Patient state over time by unit. The patient population progresses through infection over three months (with 80% infected). The bold line shows the median results of 30 trials, and the fainter lines show the minimum and maximum from the 30 trials.”</figcaption>
<figcaption>Figure 3. “Progression of patient population through COVID infection, assuming 80% become infected over three months, with 15% mortality. The figure also shows the number of patients not allocated to a dialysis session at any time. The bold line shows the median results of 30 trials, and the fainter lines show the minimum and maximum from the 30 trials.”</figcaption>
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<p><strong>Figure 3</strong> - similar to figure 2, but with different categories and divided by unit <imgsrc="../original_study/article_fig3.png" class="img-fluid" alt="Figure 3"></p>
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<p><strong>Figure 4</strong> - patients displaced from current unit and travel times added</p>
<figcaption>Figure 4. “Patient displacement. The number of patients displaced from their current unit (left panel) and the additional travel time to the unit of care (right panel) for displaced patients. These results do not include those receiving inpatient care. The patient population progresses through infection over three months (with 80% infected). The bold line shows the median results of 30 trials, and the fainter lines show the minimum and maximum from the 30 trials.”</figcaption>
<p><strong>From results section:</strong> “In the planned strategy of using half of one of the largest units (Queen Alexandra) for COVID-positive dialysis outpatients, and then using a second unit (Basingstoke, also provid- ing up to half of its capacity for COVID-positive dialysis outpatient patients) for any excess, the dialysis system copes without any patients being unable to be allocated to a session (or without any need in dropping dialysis frequency). Workload in units that do not take COVID- positive outpatients will fall during the outbreak (though some work will flow back to them if they need to care for COVID-negative patients displaced from the units caring for COVID- positive patients).”</p>
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<p><strong>Why this is in scope:</strong> The figures do not provide any information on the count of unallocated patients. The change in dialysis frequency is assumed to be an implicit consequence of whether or not there are any unallocated patients necessitating they wait a day or change frequency in the model.</p>
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<p><strong>Exactly what we are aiming to reproduce from this statement:</strong> Observation of no patients being unable to be allocated to a session.</p>
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</section>
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<sectionid="not-in-scope" class="level3">
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<h3class="anchored" data-anchor-id="not-in-scope">Not in scope</h3>
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<p><strong>Figure 1</strong> - as it is a flow chart representing of pathways in model.</p>
<figcaption>Figure 5. “One-way ambulance transport time distributions (1000 model runs). Results compare population COVID-positive and ambulance seating capacity (e.g. 2 = 2 seats.) Figures do not include ambulance clean-down/turnaround time.”</figcaption>
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<p><strong>Figure 6</strong> - as it is a result from the Markov model</p>
<figcaption>Figure 6. “Two-way ambulance transport time distributions (1000 model runs). Results compare population COVID-positive and ambulance seating capacity (e.g. 2 = 2 seats.) Figures do not include ambulance clean-down/turnaround time.”</figcaption>
<h2class="anchored" data-anchor-id="key-results-from-elsewhere-in-article">Key results from elsewhere in article</h2>
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<p>“In the planned strategy of using half of one of the largest units (Queen Alexandra) for COVID-positive dialysis outpatients, and then using a second unit (Basingstoke, also provid- ing up to half of its capacity for COVID-positive dialysis outpatient patients) for any excess, the dialysis system copes <strong>without any patients being unable to be allocated to a session (or without any need in dropping dialysis frequency)</strong>. Workload in units that do not take COVID- positive outpatients will fall during the outbreak (though some work will flow back to them if they need to care for COVID-negative patients displaced from the units caring for COVID- positive patients).”</p>
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<li>This is in scope, as the figures don’t provided any information on the count of unallocated patients, or therefore on whether any therefore had changes in dialysis frequency (which assuming is the implicit conclusion of being unallocated changing frequency). Hence, aim to reproduce: <strong>observation of no patients being unable to be allocated to a session</strong></li>
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