HBOT has a better cognitive outcome than NBH for patients with mild traumatic brain injury: A randomized controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: Normobaric hyperoxia (NBH) and hyperbaric oxygen therapy (HBOT) are effective treatment plan for traumatic brain injury (TBI). The aim of this study was to compare cognitive outcome after mild TBI between NBH and HBOT so as to provide a more suitable treatment strategy for patients with mild TBI. METHODS: A prospective research was conducted between October 2017 and March 2023, enrolling patients with mild TBI (Glasgow coma scale score: 13-15 points) within 24 hours of injury in Cangzhou Central Hospital. Patients were randomized into 3 groups: group control (C), group NBH and group HBOT. The patients in HBOT group received hyperbaric oxygen therapy in high pressure oxygen chamber and patients in NBH group received hyperbaric oxygen therapy. at 0 minute before NBH or HBOT (T1), 0 minute after NBH or HBOT (T2) and 30 days after NBH or HBOT (T3), level of S100β, NSE, GFAP, HIF-1α, and MDA were determined by ELISA. At the same time, the detection was performed for MoCA and MMSE scores, along with rSO2. RESULTS: The results showed both NBH and HBOT could improve the score of MoCA and MMSE, as well as the decrease the level of S100β, NSE, GFAP, HIF-1α, MDA, and rSO2 compared with group C. Furthermore, the patients in group HBOT have higher score of MoCA and MMSE and lower level of S100β, NSE, GFAP, HIF-1α, MDA, and rSO2. CONCLUSION: Both NBH and HBOT can effectively improve cognitive outcome for patients with mild TBI by improving cerebral hypoxia and alleviating brain injury, while HBOT exert better effect than NBH.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".