Hyperbaric Oxygen Therapy for Difficult Wound Healing in Newfoundland and Labrador \n
Bibliographic record
Abstract
Research Question: \n \nWhat does the scientific literature tell us about the clinical and economic effectiveness of hyperbaric oxygen treatment for difficult wound healing (i.e., diabetic foot ulcers, pressure ulcers, delayed radiation-induced injury, thermal burns, skin grafts and flaps, and revascularization after organ transplantation) considering the expected patient populations and given the social, geographic, economic and political contexts of Newfoundland and Labrador? \n \nResults: \n \nResearch evidence supports HBOT as clinically-effective and cost-effective for treating diabetic foot ulcers. While evidence supports HBOT as clinically-effective for treating delayed radiation-induced injuries of the head, neck and pelvis, there is insufficient evidence as to whether HBOT is cost-effective for the same injuries. \n \nThere is insufficient evidence about the clinical or cost effectiveness of HBOT for: pressure ulcers, delayed radiation-induced injuries in other parts of the body, thermal burns, skin grafts, skin flaps, or revascularization after organ transplantation. \n \nThe cost effectiveness of HBOT for appropriate non-healing wounds increases as the number of treated patients increases. \n \nThe appropriate and timely referral of patients for HBOT treatment improves with integration of wound-care management into existing chronic and acute health service programs. \n \nOverall, evidence for the clinical and cost effectiveness of HBOT for non-healing wounds is limited. As a result, future studies will be needed to augment the evidence base concerning HBOT for a number of conditions. \n \n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".