Bibliographic record
Abstract
ImageMany people complain that the health care industry has been slow to use technology in ways that banking, travel, and others pioneered years ago. Peter Yang, MD, a hematology/oncology fellow at Beth Israel Deaconess Medical Center, decided to do something about it. “I've been frustrated that information-sharing and collaboration in medicine lags far behind other industries,” he said. “I believe that doctors deserve to have the same robust platforms that other groups get to use to share information and work together.” That is why he created HemOnc.org, a wiki that allows medical professionals to take notes, share information, and learn from one another. The free resource, which came online in November 2011, is building a knowledge base around these priorities: A database of chemotherapy agents and other medications; An index of diseases; A collection of chemotherapy regimens and direct links to the primary literature that supports them; Sample order sets and examples of supportive medications used with treatment regimens; and Links to other resources by disease, such as information about prognosis, clinical calculators, staging, and patient resources; He invites all hematologists, oncologists, and other medical professionals to create a log-in account on the site and share their own knowledge. What is a wiki? “Basically, a wiki is a website that allows people to collaborate on the Internet by creating documents together through their web browsers. HemOnc.org is built upon a model similar to that for Wikipedia; if somebody sees an article and has something to add or clarify, they can do that. There is one important distinction from Wikipedia, though: the only way people can change information on the site is to create accounts and contact me to activate them. After I make sure that someone is a medical professional and has a background in hematology/oncology, the person is then given access to edit the site.”PETER YANG, MD: “Doctors deserve to have the same robust platforms that other groups get to use to share information and work together.”Why does hematology/oncology need a wiki? “There is a lot of good information already available, but the main way people learn about things in medicine is a little bit old-fashioned. Oftentimes things are shared by word of mouth or if you are in the audience when someone makes a particular presentation. I am hoping that this will be something that will really help people share information and learn from the experiences of others. “The site lets physicians record notes for themselves and access information prepared by their peers and jump to the primary reference with just one click.” Who is usingHemOnc.org? “A few hundred people per day are currently using the site. About 70 percent of the users are from the United States, and the greatest number of international users come from India, United Kingdom, Canada, Australia, and Germany. The site appears to have a pretty good mix of oncologists from academic centers as well as community practices.” How doesHemOnc.orgsupplement the information already available to oncologists? “I was working with a software company to develop clinical decision support software, and I needed to fact-check all of their chemotherapy regimens. When I turned to the primary literature and third-party references—such as textbooks, pocket handbooks, online references, and literature from some large oncology organizations—I was surprised and disappointed to find more errors than I would have expected. There were even mistakes within the original peer-review papers, such as the abstract or figure containing different information from the body text. So I wanted to make a free reference where users could correct any errors they found. “I want to encourage people to review the primary literature so that they can have as deep of an understanding of the topics as possible. HemOnc.org makes it convenient by making the original papers or other documentation such as package inserts just a click away. Whenever we notice inconsistencies or questionable information in the literature, this is described on the site as transparently as possible. “For example, if a paper lists a different dosage in a figure compared with the body text, this is pointed out. There are no deviations at all from the references; if a dosage and schedule are listed on the site, that was exactly what was in the original reference. This is in contrast to some other guidelines and references, which sometimes use slightly different dosage ranges based on the personal experience of the people who put together the guidelines. That is a reasonable thing for some groups to do, but that is not what HemOnc.org aims to do. Instead, it is trying to help people reference the original literature as easily as possible.” How can readers trust the information onHemOnc.org? “The wiki model creates an environment where information can be as accurate as possible. First, people who wish to add information to HemOnc.org must be approved as medical professionals before they can contribute anything; that reduces the chance of unqualified contributors, vandalism, or spam. The site always tracks which person makes every single change, so that provides accountability. And all users can immediately correct any errors that they find. So, if I make a typo, any user can correct that mistake. “Ultimately, though, trust must be earned, and I would invite users to see for themselves if they find this to be a trustworthy resource.” Part 3 of a Series This article is part of a continuing look at how information technology is changing the practice of oncology. A collection of the series to date is available at: http://bit.ly/OT-HITCollection iPad Exclusive! PODCAST: Listen on the iPad edition of this issue as Peter Yang explains how it was actually errors in the medical literature that prompted his decision to create HemOnc.org.ImageTo receive our iPad issues, download the free Oncology Times app from the App Store today! Visithttp://bit.ly/OT-iPadApp, search in the App Store, or follow the link ononcology-times.com.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".