Ten years of major equestrian injury: are we addressing functional outcomes?
Bibliographic record
Abstract
BACKGROUND: Horseback riding is considered more dangerous than motorcycle riding, skiing, automobile racing, football and rugby. The integral role of rehabilitation therapy in the recovery of patients who have sustained a major horse-related injury is previously not described. The goals of this paper were to (1) define the incidence and pattern of severe equestrian trauma, (2) identify the current level of in-patient rehabilitation services, (3) describe functional outcomes for patients, and (4) discuss methods for increasing rehabilitation therapy in this unique population. METHODS AND RESULTS: A retrospective review of the trauma registry at a level 1 center (1995-2005) was completed in conjunction with a patient survey outlining formal in-hospital therapy. Forty-nine percent of patients underwent in-patient rehabilitation therapy. Injuries predictive of receiving therapy included musculoskeletal and spinal cord trauma. Previous injury while horseback riding was predictive of not receiving therapy. The majority (55%) of respondents had chronic physical difficulties following their accident. CONCLUSION: Rehabilitation therapy is significantly underutilized following severe equestrian trauma. Increased therapy services should target patients with brain, neck and skull injuries. Improvements in the initial provision, and follow-up of rehabilitation therapy could enhance functional outcomes in the treatment resistant Western equestrian population.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".