Assessing prefrontal cortex oxygenation after sport concussion with near‐infrared spectroscopy
Bibliographic record
Abstract
Summary Background Clinicians typically rely on neuropsychological and balance tests to track concussion recovery. The aforementioned tests imply impairments that are based on performance, but these tests do not directly measure brain physiology throughout concussion recovery. Because of these issues, an objective biomarker that can index severity and the recovery timeline is needed. Moreover, the number of concussions occurring at a recreational level requires the biomarker to be easily administered in a cost effective manner, and the results easily interpreted. Methods To address these issues, near‐infrared spectroscopy was used to assess the relative changes in oxy (HbO2)‐ and deoxyhaemoglobin and the associated standard deviations (SD) in the prefrontal cortex. Resting haemoglobin, and haemoglobin changes in response to hypercapnia (five repeated 20s breath holds), was measured in all participants. Data were aggregated into healthy baselines (n = 115), and concussed participants on days 1–3 (n = 14), 4–6 (n = 8), and 7–14 (n = 11). The data were statistically compared using a 1 × 4 ANOVA. Results Resting HbO2 values progressively lowered from days 1–3 to 7–14 (with no differences compared to controls). The second major finding showed that hypercapnic HbO2 SD was significantly lower than resting values on days 1–3 and 4–6, but reversed back towards the healthy control group on day 7–14. Conclusion Monitoring cerebral oxygenation changes is a viable biomarker to assess the physiological state of the brain following concussion.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".