The role of routine pulmonary imaging before hyperbaric oxygen treatment
Bibliographic record
Abstract
Respiratory injury during or following hyperbaric oxygen treatment (HBOT) is rare, but associated pressure changes can cause iatrogenic pulmonary barotrauma with potentially severe sequelae such as pneumothoraces. Pulmonary blebs, bullae, and other emphysematous airspace abnormalities increase the risk of respiratory complications and are prevalent in otherwise healthy adults. HBOT providers may elect to use chest X-ray routinely as a pre-treatment screening tool to identify these anomalies, particularly if a history of preceding pulmonary disease is identified, but this approach has a low sensitivity and frequently provides false negative results. Computed tomography scans offer greater sensitivity for airspace lesions, but given the high prevalence of incidental and insignificant pulmonary findings among healthy individuals, would lead to a high false positive rate because most lesions are unlikely to pose a hazard during HBOT. Post-mortem and imaging studies of airspace lesion prevalence show that a significant proportion of patients who undergo HBOT likely have pulmonary abnormalities such as blebs and bullae. Nevertheless, pulmonary barotrauma is rare, and occurs mainly in those with known underlying lung pathology. Consequently, routinely using chest X-ray or computed tomography scans as screening tools prior to HBOT for low-risk patients without a pertinent medical history or lack of clinical symptoms of cardiorespiratory disease is of low value. This review outlines published cases of patients experiencing pulmonary barotrauma while undergoing pressurised treatment/testing in a hyperbaric chamber and analyses the relationship between barotrauma and pulmonary findings on imaging prior to or following exposure. A checklist and clinical decision-making tool based on suggested low-risk and high-risk features are offered to guide the use of targeted baseline thoracic imaging prior to HBOT.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".