Bibliographic record
Abstract
The use of long-term home oxygen therapy (HOT) has become increasingly common for treatment of chronic pulmonary diseases. Although illegal to smoke while on HOT, there is an increasing incidence of burn injuries in those patients who smoke while on HOT. The importance of recognition of the prevalence of this injury, the obstacles faced when treating these patients, and understanding the proposed algorithmic approach to be taken with patients on HOT, including prescription, reassessment, and prevention of burn injury are outlined in this review. Retrospective epidemiological data including circumstances, admission, treatment, and disposition were collected and reviewed on the patients treated from 1999 to 2008 with burns secondary to smoking while on HOT. Seventeen patients sustained injuries secondary to smoking on HOT over the 9-year period; 9 patients were female and 8 were male. All the patients were on HOT for chronic obstructive pulmonary disease. Mean patient age was 69.1 ± 2.5 years and mean TBSA 2.8 ± 0.4%; 11.8% (2/17) sustained inhalation injury requiring intubation and 23.5% (4/17) required wound debridement and skin grafting. Mean hospital stay was 42.8 ± 12.5 days; 10.3 ± 5.4 days in the burn intensive care unit and 32.5 ± 11.0 days in the ward. Before the burn injury, 23.5% (4/17) lived in long-term care facilities. On discharge from hospital, 47.1% (8/17) were transferred to extended care facilities or other acute care hospitals, and 11.8% (2/17) died during their hospitalization. After recovery, there was a 35.3% reduction in patients able to return home and/or live independently. A significant number of burn injuries secondary to smoking while on HOT was observed. These patients differ from standard burn patients because they are older in age, have higher rates of inhalation injury, and have much longer lengths of hospitalization, despite smaller TBSA injuries. Prevention of this injury would improve the safety of the patient and those around them as well as healthcare resource allocation. A proactive multidisciplinary algorithmic approach is presented which can be used to manage patients on HOT at risk for continued smoking to decrease the incidence and the impact of burn injuries in this patient population.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".