The Effect of Hyperbaric Oxygen Therapy on Psychological State and Wound Healing: A Case Report
Bibliographic record
Abstract
BACKGROUND: Hyperbaric oxygen therapy (HBOT), in which patients receive high concentrations of oxygen in a pressurized chamber, has been used in clinical practice to improve wound healing. More recent applications of HBOT have resulted in successful management of a wide range of conditions; however, the psychosomatic factors associated with these conditions remain understudied and require clarification. PURPOSE: To investigate the effects of HBOT in a female patient without diabetes who presented with an atypical wound of 9 years' duration with no sign of healing as well as with psychosomatic factors. CASE REPORT: The patient underwent 20 once-daily sessions of HBOT for 120 minutes per session every Monday through Friday for 4 weeks at 2.4 ATA (atmosphere absolute pressure) and received daily dressing changes with a nonadherent dressing containing silver, alginate, and carboxymethylcellulose. The 36-Item Short Form Health Survey and the Hospital Anxiety and Depression Scale quality-of-life questionnaires were administered before treatment and after 1 year of treatment. HBOT resulted in complete lasting wound remission as well as subjective improvement in quality of life and in levels of anxiety and depression. CONCLUSION: HBOT has known therapeutic effects on wound healing, and it may also have a substantial effect on psychosomatic mechanisms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".