"What have I got to lose?": An analysis of stem cell therapy patients' blogs
Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».