Bibliographic record
Abstract
This year, at the American Physical Therapy Association's Combined Sections Meeting (CSM), the Academy of Cardiovascular and Pulmonary Physical Therapy showed once more to be a meaningful community. As usual, we gathered to learn from clinicians, researchers, and educators who keep our profession (and the cardiopulmonary specialty) alive. Combined Sections Meeting delivered excellent presentations, from educational sessions to platform presentations and posters. However, it was the sense of community around the Academy what was the highest point at CSM. First, we celebrated the 50 years of the Academy. Second, we welcomed a new Academy President, Dr. Ashley Parish and we tanked Dr. Angela Campbell for her 2-term presidency. Finally, and by far the most impactful, we raised awareness about domestic violence celebrating the life and accomplishments of Dr. Christa Nicole Bauer, who received the Clinical Excellence Award. It was an emotional moment that will be remembered for ever. In this issue, we are closing our special issue on long-COVID with a few articles that were not ready for production by the January's issue. First, Dr. Del Carpio-Orantes, from Veracruz-Mexico, found our special issue on long-COVID very interesting,1 especially the case report by Wright et al.2 where they treated their patient with enoxaparin. Second, Goosen et al.3 used wearable devices to help patients with long-COVID to pace themselves. Then, Lopes Sauers et al.4 performed a systematic review to determine the outcome measures used in patients with long-COVID 1 year after the pandemic. In addition, McDuff et al.,5 from the Physio International Forum, present here the priorities for research, education, clinical practice, and policy that should be addressed for patients with long-COVID. Finally, 2 research reports not related to long-COVID close this issue. Gabison et al,6 from Dr. Reid's group in Canada, studied the muscle recruitment in the neck and abdomen during different respiratory exercise to help patients recovering from mechanical ventilation. And Wu et al.7 performed a very elegant study to determine the association between different genotypes of cystic fibrosis and muscle function, supporting resistance exercise training in these patients. The great work presented in this issue and the impact that our profession and our Academy have confirm an extraordinary sense of belonging: belonging to a Meaningful Community.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.005 | 0.041 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.328 | 0.149 |
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".