Adaptation of the CARE Guidelines for Therapeutic Massage and Bodywork Publications: Efforts To Improve the Impact of Case Reports
Bibliographic record
Abstract
Case reports provide the foundation of practice-based evidence for therapeutic massage and bodywork (TMB), as well as many other health-related fields. To improve the consistency of information contained in case reports, the CARE (CAse REport) Group developed and published a set of guidelines for the medical community to facilitate systematic data collection (http://www.care-statement.org/#). Because of the differences between the practice of medicine and TMB, modifying some sections of the CARE guidelines is necessary to make them compatible with TMB case reports. Accordingly, the objectives of this article are to present the CARE guidelines, apply each section of the guidelines to TMB practice and reporting with suggested adaptations, and highlight concerns, new ideas, and other resources for potential authors of TMB case reports. The primary sections of the CARE guidelines adapted for TMB case reports are diagnostic assessment, follow-up and outcomes, and therapeutic intervention. Specifically, because diagnosis falls outside of the scope of most TMB practitioners, suggestions are made as to how diagnoses made by other health care providers should be included in the context of a TMB case report. Additionally, two new aspects of the case presentation section are recommended: a) assessment measures, which outline and describe the outcome measures on which the case report will focus, and b) a description of the TMB provider (i.e., scope of practice, practice environment, experience level, training, credentialing, and/or expertise) as part of the intervention description. This article culminates with practical resources for TMB practitioners writing case reports, including a TMB Case Report Template-a single document that TMB practitioners can use to guide his or her process of writing a case report. Once the template is adopted by authors of TMB case reports, future efforts can explore the impact on the quality and quantity of case reports and how they impact TMB practice, research, education and, ultimately, the clients.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".