Telemedicine-guided forearm emergency decompressive fasciotomy for compartment syndrome
Bibliographic record
Abstract
INTRODUCTION: We highlight the utility of telemedicine and telementoring for the management of orthopaedic emergencies using a case of forearm compartment syndrome following a penetrating trauma in a northern Inuit community in Nunavik, Quebec, Canada. METHODS & RESULTS: As in many cases of compartment syndrome in rural settings, the patient was at a high risk of developing irreversible complications. A prompt diagnosis followed by an emergency decompressive fasciotomy was warranted. Using telemedicine and telementoring guidance, the diagnosis of compartment syndrome was made, and the patient's volar compartment was successfully decompressed by a local emergency physician in a timely manner. Subsequently, the patient was able to be safely transferred to a level 1 trauma centre for further surgical management. This included a second-look operative exploration, irrigation and debridement, completion of volar fasciotomy and ulnar nerve decompression. No complications were seen. DISCUSSION: Our experience highlights two important clinical implications. First, telemedicine can be successfully implemented to facilitate clinical diagnosis of surgical emergencies in the rural setting. Second, telementoring can effectively allow surgeons to guide physicians remotely to perform emergency decompressive fasciotomy, which can help salvage the affected limb and significantly decrease the risk of debilitating complications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".