"What have I got to lose?": An analysis of stem cell therapy patients' blogs
Bibliographic record
Abstract
Introduction The promises of cell research have provided hope to those who suffer from a variety of diseases and conditions, but to date, there are very few proven therapies that involve cells. (1) However, a growing number of clinics around the globe have begun offering cell treatments for a wide range of conditions. While it is difficult to know exact numbers, previous studies have suggested that possibly thousands of people travel abroad every year to receive cell therapy for a vast array of conditions, and all at a hefty cost. (2) This growing phenomenon has been dubbed stem cell equating it with other forms of medical tourism that marry facelifts and beach vacations. This terminology has been criticized for ignoring the often serious and desperate conditions in which many cell therapy patients find themselves. (3) Whatever the name, these unproven cell therapies have provided hope for many and drawn criticism from the scientific community as well as many others. (4) Much of the criticism is based on the lack of evidence gathered through clinical trials regarding the safety and efficacy of these treatments. (5) Also, a lack of transparency in treatment protocols at the clinics and no apparent post-treatment follow-up has raised concerns about the adverse effects and risks of the treatment. (6) Other issues have been raised including the lack of true informed consent (7) and vulnerable people {including children) being taken advantage of or put at risk, (8) to name a few. Due to this lack of transparency on the part of these clinics it is also difficult to assess how many people are pursuing treatment, where they are going, why they are going, and what their experiences are during treatment and recovery. Personal blogs, written by patients or their caregivers, provide a unique method for gaining an understanding into the motivations for pursuing treatment and the actual experiences of preparing for and undergoing cell therapies abroad. (9) Methods In order to gain more insight into patients and/or their caregivers who choose to pursue unproven cell therapies in overseas clinics, we conducted a thematic analysis of publicly available blogs written either by patients or their caregivers who plan on, are in the midst of, or have received cell therapy at an overseas clinic. Personal blogs were collected using the Google Blog Search engine with the following search terms: cell treatment or cell therapy, as well as travel, overseas, abroad, tourism, or variations of these terms (e.g., travel or traveling). No date restrictions were used and the search was restricted to English-language blogs only. Some of the cell clinics host patient blogs on the clinic website to help promote the success of their therapies; however, we excluded these blogs from our sample so as to limit the chances of bias presented in the data. In total, we collected a sample of 30 blogs, which included the experiences of 32 patients. Two researchers conducted an initial analysis of a random sample of 10 blogs. Each researcher analyzed 5 blogs independently by collecting patient demographic information, writing notes about patients' reasons for pursuing treatment, experiences at the clinic and during recovery, as well as any other information relevant to the patient experience with cell therapy. From these notes, the researchers developed a list of common themes from across the blogs. The researchers' lists of themes were then compared, and a list of 7 themes based on both of the analyses was compiled. One of the researchers then analyzed the remaining 20 blogs, adding to the list of themes for a total of 10 themes. Findings Demographics The purpose of this study was not to construct a complete picture of the range of patients that pursue cell therapy abroad. (10) However, the demographics of the patients presented in the blogs bears some consideration. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".