Gordie Howe’s “Miraculous Treatment”: Case Study of Twitter Users’ Reactions to a Sport Celebrity’s Stem Cell Treatment
Bibliographic record
Abstract
BACKGROUND: Former Detroit Red Wing Gordie Howe received stem cell (SC) treatment in Mexico in December 2014 for a stroke he suffered in October 2014. The news about his positive response to the SC treatment prompted discussion on social networks like Twitter. OBJECTIVE: This study aims to provide information about discussions that took place on Twitter regarding Howe's SC treatment and SC treatment in general. In particular, this study examines whether tweets portrayed a positive or negative attitude towards Howe's SC treatment, whether or not tweets mention that the treatment is unproven, and whether the tweets mention risks associated with the SC treatment. METHODS: This is an infodemiology study, harnessing big data published on the Internet for public health research and analysis of public engagement. A corpus of 2783 tweets about Howe's SC treatment was compiled using a program that collected English-language tweets from December 19, 2014 at 00:00 to February 7, 2015 at 00:00. A content analysis of the corpus was conducted using a coding framework developed through a two-stage process. RESULTS: 78.87% (2195/2783) of tweets mentioned improvements to Howe's health. Only one tweet explicitly mentioned that Howe's SC treatment was unproven, and 3 tweets warned that direct-to-consumer SC treatments lacked scientific evidence. In addition, 10.31% (287/2783) of tweets mentioned challenges with SC treatment that have been raised by scientists and researchers, and 3.70% (103/2783) of tweets either defined Howe as a "stem cell tourist" or claimed that his treatment was part of "stem cell tourism". In general, 71.79% (1998/2783) of tweets portrayed a positive attitude towards Howe's SC treatment. CONCLUSIONS: Our study found the responses to Howe's treatment on Twitter to be overwhelmingly positive. There was far less attention paid to the lack of scientific evidence regarding the efficacy of the treatment. Unbalanced and uncritical discussion on Twitter regarding SC treatments is another example of inaccurate representations of SC treatments that may create unrealistic expectations that will facilitate the market for unproven stem cell therapies.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".