Building a multidisciplinary team for burn treatment - Lessons learned from the Montreal tendon transfer experience.
Bibliographic record
Abstract
Multidisciplinary teams (MDTs) represent a recognized component of care in the treatment of complex conditions such as burns. However, most institutions do not provide adequate support for the formation of these teams. Furthermore, the majority of specialists lack the managerial skills required to create a team and have difficulties finding the proper tools. Our objective is to provide an insight for health care professionals, who wish to form a MDT for burn treatment, on the challenges that are likely to be faced, and to identify key elements that may facilitate the establishment of such a project. The setting for this was a plastic surgery department and rehabilitation center at a national reference center. A qualitative analysis was performed on all correspondences related to our tetraplegia project, from 2006 to 2008. To guide our thematic analysis, we used a form of systems theory known as the complexity theory. The qualitative analysis was performed using the NVivo software (Version 8.0 QSR International Melbourne, Australia). Lastly, the data was organized in chronologic order. Three main themes emerged from the results: knowledge acquisition, project organizational setup and project steps design. These themes represented respectively 24%, 50% and 26% of all correspondences. Project steps design and knowledge acquisition correspondences increased significantly after the introduction of the mentor team to our network. We conclude that an early association with a mentor team is beneficial for the establishment of a MDT.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".