A comparison of heterotopic ossification treatment within the traumatic brain and spinal cord injured population: An evidence based systematic review
Bibliographic record
Abstract
BACKGROUND: To compare the treatment of heterotopic ossification (HO) within the traumatic brain and spinal cord injured populations. METHODS: MEDLINE/Pubmed, CINAHL, EMBASE, and PsycINFO databases were searched for articles addressing treatment of HO post-injury. Articles were constrained to: English language and human subjects. Studies were included if: n ≥ 50% of the subjects had a spinal cord injury (SCI) or a traumatic brain injury (TBI), n ≥ 3 SCI or TBI subjects, and study subjects participated in a treatment or intervention. Study quality, for randomized control trials (RCTs), were assessed using the PEDro assessment scale, while non-RCTs was assessed using the Downs and Black evaluation tool. A modified Sackett scale was used to apply levels of evidence for each intervention. RESULTS: In total 26 studies (NTBI = 12; NSCI = 14) met inclusion criteria. The majority of studies (10/12) conducted in the TBI population were surgical interventions. Studies conducted with the SCI population investigated diverse pharmacological treatments including: bisphosphonates, non-steroidal anti-inflammatory drugs (NSAIDs) and Warfarin. Non-pharmacological studies investigated the benefits of pulse low-intensity electromagnetic field therapy, surgical excision, and radiotherapy in the treatment of HO. CONCLUSIONS: Within the SCI literature, NSAIDs showed the greatest efficacy in the prevention of HO when administered early after a SCI, and biphosphonates were found to be the most effective treatment strategy. In the TBI population, surgical excision was the most effective treatment.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".