Effect of hyperbaric oxygen treatment on patients with reduced left ventricular ejection fraction
Bibliographic record
Abstract
INTRODUCTION: Hyperbaric oxygen treatment (HBOT) is available to a wide spectrum of patients, many with significant co-morbidities. Considering its effects on cardiac physiology and reports of pulmonary oedema following exposure, concerns exist about the safety of patients with compromised cardiac function. Few studies have described adverse events occurring during HBOT and even fewer reports address events arising in the hours following HBOT. A relation between adverse events and cardiac function has not been established. As medical guidance is limited, we aimed to evaluate the risk for patients with reduced left ventricular ejection fraction (LVEF) receiving HBOT. METHODS: This retrospective chart review of patients receiving HBOT from April 2003 through December 2019 at our hospital was designed to describe clinical characteristics of patients and to identify adverse events during HBOT and within 24 hours after HBOT. Patients ≥ 40 years of age with a documented LVEF of ≤ 40% were included. Data are presented as mean (SD) [range] or counts, as appropriate. RESULTS: A total of 23 patients were included in the final analysis, 2 (1) [0-4] patients per year. Patients received 25 (19) [1-60] treatments. Two patients had an episode of acute decompensated heart failure possibly linked to HBOT. CONCLUSIONS: This study described the clinical characteristics of patients with reduced LVEF receiving HBOT and showed reassuring results, with a majority of patients with reduced LVEF tolerating HBOT well. Prospective research is required to more fully assess the risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".