Given a paragraph, generate a claim that is supported by the given paragraph. 1) The claim must contain information from within the paragraph. 2) A sentence within the paragraph can be used as a claim. 3) The claim should not have contradictions within the paragraph. 4) The claim should be at most one sentence long.

Example Input: This is a tricky one. The story does not explicitly address cost, except to call text messaging “a scalable and affordable approach.” For many (or most) patients, the cost of receiving four text messages a week would likely be negligible — covered by an individual’s phone plan or by nominal text rates. However, not everyone owns a mobile phone, a fact that makes access (and cost) a more significant issue. However, the story does deliver on this point. The story discusses both the number of U.S. adults who own mobile phones (90 percent) and the fact that text messages are more accessible than other forms of mobile health communication (such as apps, which require patients to own smartphones or other smart devices). So, while we’d prefer a more in-depth discussion here, we’ll award a Satisfactory rating for the story’s nod in this direction. The story addresses the range of benefits reported in the study, such as reduced cholesterol and blood pressure, but doesn’t quantify that information or offer readers any context. For example, while the story says “patients who had received the text messages had reduced their cholesterol,” the JAMA paper reports that patients who received the text messages had a “mean difference [of] −5 mg/dL” in “LDL-C.” In other words, the information in the paper, while quantified, probably isn’t very useful to many readers outside the health industry. Is “a mean difference of −5 mg/dL in LDL-C” good? Bad? Irrelevant? This is where reporters have an opportunity to shine. If a news story can take those numbers and translate them into language that is accessible to non-experts, it provides a very real service. It would have been great to see that here. As long as people aren’t driving and checking their cell phones, there don’t appear to be any harms associated with receiving well-meaning text messages four times a week, so we’ll mark this one “not applicable.” This is a strong suit. The story not only offers a concise, but good, summary of the study — but it also goes out of its way to address the study’s limitations. For example, the study notes that the study “can’t tell us whether text messaging could eventually lead to fewer subsequent heart attacks among the patients receiving those messages or whether the positive health outcomes would continue past the six-month study window.” Good points, and we’re glad the story includes them. No disease mongering here. The story includes input from an outside expert who was not involved with the research. The story clearly meets our bar here, although it would have been stronger if it had reached out to at least one more independent source — preferably one who hadn’t written an editorial that ran in conjunction with the paper in JAMA. Yes, the editorial was independent and provides some insightful commentary. But it also means we already knew where this source stood on the issue. Finding another reputable expert, who hadn’t already weighed in publicly on the work, would have been great. The focus of the story is not so much on how the text message intervention directly reduced heart disease risk, but on the extent to which it resulted in patients taking steps to reduce their own risk — such as by exercising more, smoking less, making dietary changes, or taking prescription drugs on a regular basis. The story does discuss other “mobile health interventions,” such as smartphone apps — but notes that there don’t appear to be many mobile health projects that rely simply on text messages. The story also looks at how the text messages helped patients pursue lifestyle changes — like exercising more and smoking less — that reduced their risk. It was fairly clear from the story that this was a clinical trial, and that this particular service doesn’t seem to be available to offer customized, encouraging text messages for heart disease patients. From the start, the story frames this research in the context of the much larger “mobile health” field. For example, the story notes that “While there are more than 100,000 health-related apps on the market, the evidence to date is lacking as to whether many of those apps are safe or effective.” Since the story includes quotes from an interview, we can be fairly certain it went beyond the related JAMA news release.
Example Output: Text Reminders Help People Lower Cholesterol, Blood Pressure

Example Input: Collins, a Republican, introduced the proposal with Democratic Minnesota Sen. Tina Smith. It’s also designed to beef up prevention, diagnostics and treatment for tick-borne diseases, and it’s headed to the full Senate next. The proposal’s named the Kay Hagan Tick Act in honor of former Sen. Kay Hagan of North Carolina who died in October of complications from Powassan virus, a tick-borne infection. Cases of Lyme disease nearly doubled in Maine from 2010 to 2018. Collins describes the surge of tick-borne diseases as a “burgeoning public health crisis.” The proposal would require the U.S. Department of Health and Human Services to develop a national strategy about the diseases.
Example Output: Collins bill to combat Lyme and other tick diseases moves on.

Example Input: "The story notes that TXA is cheap, but we wish it had put a dollar amount on it the way competing coverage did. The cost of the medication was cited in the press release put out by the London School of Hygiene and Tropical Medicine and could easily have been included. ""Cheap"" is relative. And, pertinent to this issue, what’s ""cheap"" in the West may not be so cheap in developing countries. Unlike some competing coverage, this story describes the benefits of TXA in both relative and absolute terms, providing readers with an accurate view of the magnitude of the effect observed in the study. Unfortunately, the story loses this balance when it unquestioningly passes along the claim that TXA could save 100,000 lives in a year if it were used around the globe. While a back-of-the-envelope calculation may suggest that this is possible, many hurdles stand in the way of this drug being universally dispensed for trauma cases worldwide. It’s also far from clear that the findings of this study can be replicated under everyday conditions in emergency departments around the globe. The story should have tapped an expert to help put this pie-in-the-sky figure in a more appropriate context for readers. The story correctly states that the researchers observed no increase in clots in the patients receiving TXA, earning enough for a satisfactory. However, we wish it had included, as the AP did, a caveat regarding the difficulty of identifying clots in trauma patients who subsequently die. Similar to the competing coverage from the AP and NPR, this story provided the essentials but lacked sufficient discussion of limitations. To wit: Can a study that includes data from India, China, Nigeria and many other developing countries be generalized to emergency department patients in the United States? No disease-mongering here. The story quotes the author of an accompanying editorial who was not affiliated with the study and does not appear to have conflicts regarding the research. Unlike the competing coverage from the AP and NPR, this story mentions the fact that there are other drugs which help promote clotting and might be useful for trauma patients. The story correctly warns that the benefits of TXA can’t be generalized to other drugs without additional research. The story notes that TXA is widely available around the world and easily administered. The story quotes an expert who says that using TXA in trauma patients is a new idea. The approach does appear to be novel. The story quotes two expert sources, one of whom was not mentioned in the press release issued to promote the study. This is enough to rule out the possibility that the story was based entirely on a release."
Example Output:
Drug That Stops Bleeding Could Save Lives