PHYSICAL ASSESSMENT IN SURFERS: GUIDELINES FOR HEALTH PROFESSIONALS - PART 2 LOWER QUARTER
Bibliographic record
Abstract
Musculoskeletal injuries in the lower quarter during surfing are primarily associated with the sport's fundamental movements. This movements occur when the surfer is standing on the board, riding the wave. The injuries are typically acute, with severity and complexity varying according to the surfer's skill level and maneuvers performed. The objective of the present study was to conduct an integrative review of studies related to the musculoskeletal assessment of the lower quarter, focusing on physical examinations and functional tests applicable to surfers. This integrative review was carried out through a literature review, evaluating and analyzing papers from national and international journals indexed in the scientific databases Scielo and PubMed. The developement and analysis involved a panel of experts in the field of medicine and surfing health composed of physical educators, physiotherapists and sports doctors. This guideline aims to complement the information presented in the upper quarter article, emphasizing the prevalence of musculoskeletal injuries in the lower quarter among surfers and guiding outpatient assessments. It considers the specificities of surfing and the biomechanical movements involved.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".