Consensus Statements Regarding the Multidisciplinary Care of Limb Amputation Patients in Disasters or Humanitarian Emergencies: Report of the 2011 Humanitarian Action Summit Surgical Working Group on Amputations Following Disasters or Conflict
Bibliographic record
Abstract
Limb amputations are frequently performed as a result of trauma inflicted during conflict or disasters. As demonstrated during the 2010 earthquake in Haiti, coordinating care of these patients in austere settings is complex. During the 2011 Humanitarian Action Summit, consensus statements were developed for international organizations providing care to limb amputation patients during disasters or humanitarian emergencies. Expanded planning is needed for a multidisciplinary surgical care team, inclusive of surgeons, anesthesiologists, rehabilitation specialists and mental health professionals. Surgical providers should approach amputation using an operative technique that optimizes limb length and prosthetic fitting. Appropriate anesthesia care involves both peri-operative and long-term pain control. Rehabilitation specialists must be involved early in treatment, ideally before amputation, and should educate the surgical team in prosthetic considerations. Mental health specialists must be included to help the patient with community reintegration. A key step in developing local health systems the establishment of surgical outcomes monitoring. Such monitoring can optimizepatient follow-up and foster professional accountability for the treatment of amputation patients in disaster settings and humanitarian emergencies.
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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 | 0.129 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".