Translational Research Queries, Fascia Research Congress Sequels, & Beyond . . . .
Bibliographic record
Abstract
Part of Thompson's overview focused on translational research (TR) as a cornerstone theme of the upcoming conference.As somewhat of a follow-up, the initial part of this current editorial provides, as a brief prelude to the "Highlighting" conference, an admittedly delimited discussion of TR.The editorial goes on to acknowledge certain sequels to the 2009 International Fascia Research Congress, and it then introduces additional entries in the current IJTMB issue that focus on pain management and mixed-methods research, as well as a first interview entry in the Commentaries section of the journal.The extensive and accelerated attention to TR in general over the past 10 years is indeed well documented, as is the alarming paucity of its focus when addressed specifically in the context of the massage and bodywork professions and related fields.For instance, a recent search of PubMed (conducted on 1 March 2010) using only the expression "translational research" uncovered 511 citations in the time range March 2000 to March 2005, and 2252 hits from March 2005 to March 2010.When "translational research" was combined individually with "massage," "bodywork," "massage therapy," "manual therapy," and "physiotherapy," however, the resulting number of citations across all five restrictive searches over the 10-
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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 | 0.022 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.050 | 0.022 |
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".