Bibliographic record
Abstract
This is my last editorial as editor in chief (EIC). I had a wonderful 10 years. I learned so much and will sorely miss the opportunity to preview emerging science, highlight new clinical issues, and push “hot topics.” I worked with an outstanding interprofessional and global team of experts devoted to PTJ's mission: to engage and inspire an international readership on topics related to physical therapy, to publish innovative and highly relevant content for both clinicians and scientists, and to use “a variety of interactive approaches to communicate that content, with the expressed purpose of improving patient care.” My vocabulary is insufficient to describe the qualities of the team with whom I have worked. During my tenure, PTJ was served by 52 Editorial Board members. We evolved from an Editorial Board consisting of US-based physical therapists (and one Canadian) to a board comprising international leaders in physical therapy, medicine, biomechanics, and nursing. Dr. Daniel Riddle is the only Editorial Board member who was with me since the beginning. As deputy editor, he was invaluable in helping to ensure that the journal's content is innovative and rigorous. PTJ's Editorial Board members have extraordinary expertise, wisdom, and passion for physical therapy and rehabilitation. We worked well together to improve scientific rigor and attract established and emerging scientists and clinicians to contribute to an exploding body of evidence. Supporting our efforts are hundreds of manuscript reviewers who have volunteered countless hours poring over manuscripts to provide reviews that are timely, constructive, and kind.
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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.418 | 0.350 |
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".