Extracellular Brain pH and Outcome following Severe Traumatic Brain Injury
Bibliographic record
Abstract
The ability to measure brain tissue chemistry has led to valuable information regarding pathophysiological changes in patients with traumatic brain injury (TBI). Over the last few years, the focus has been on monitoring changes in brain tissue oxygen to determine thresholds of ischemia that affect outcome. However, the variability of this measurement suggests that it may not be a robust method. We have therefore investigated the relationship of brain tissue pH (pH(b)) and outcome in patients with TBI. We retrospectively analyzed prospectively collected data of 38 patients admitted to the Neurosciences Critical Care Unit with TBI between 1998 and 2003, and who had a multiparameter tissue gas sensor inserted into the brain. All patients were managed using an evidence-based protocol targeting CPP > 70 mm Hg. Physiological variables were averaged over 4 min and analyzed using a generalized least squares random effects model to determine the temporal profile of pH(b) and its association with outcome. Median (IQR) minimum pH(b) was 7.00 (6.89, 7.08), median (IQR) maximum pH(b) was 7.25 (7.18, 7.33), and median (IQR) patient averaged pH(b) was 7.13 (7.07, 7.17). pH(b) was significantly lower in those who did not survive their hospital stay compared to those that survived. In addition, those with unfavorable neurological outcome had lower pH(b) values than those with favorable neurological outcome. pH(b) differentiated between survivors and non-survivors. Measurement of pH(b) may be a useful indicator of outcome in patients with TBI.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".