Magnitude and Clinical Predictors of Blood Pressure Changes in Patients Undergoing Hyperbaric Oxygen Therapy: A Retrospective Study
Bibliographic record
Abstract
Hyperbaric oxygen therapy (HBOT) is widely used to treat several pathologies. The hemodynamic changes during HBOT, particularly the magnitude of arterial blood pressure (ABP) increase, are not completely understood. No clinical predictors for HBOT-induced ABP increase have been described. The purpose of this study was to quantify ABP changes in patients undergoing HBOT and to examine their predictors. This retrospective longitudinal cohort study examined 3291 elective HBOT sessions. Non-invasive ABP was recorded before and after each session. The primary outcome was to quantify the HBOT-induced ABP rise. The secondary outcome was to determine the ABP-rise predictors among demographic and clinical variables. Overall, ABP increased significantly after HBOT; this finding was more evident in the hypertensive subgroup compared to the normotensive one (+6 vs. +16.2 mmHg). Clinical predictors of significant post-HBOT ABP change were history of hypertension and pre-session baseline ABP classification. This study demonstrates an absolute HBOT-induced ABP rise. This change is clinically relevant in patients with history of hypertension. A higher baseline ABP seems a risk factor for clinically relevant ABP change. Pre-session ABP should be used clinically as an indicator for strict ABP monitoring during HBOT; future studies are recommended to explore the ABP optimization before starting an HBO 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".