Expeditious formation of London Health Sciences Centre (LHSC) adult ground critical care transport team in aid of 3rd wave coronavirus disease 2019 (COVID-19) pandemic in Ontario, Canada
Bibliographic record
Abstract
Background: The third wave coronavirus disease of 2019 (COVID-19) infection resulted the highest number of intensive care unit (ICU) admissions in Toronto, Canada, which needed to decant to other hospitals in the Province of Ontario. The interfacility transport of critical and non-critical patients is the responsibility of the municipality Emergency Medical Services (EMS) and the provincially supported private company Ornge Air and Ground Ambulance Services in Ontario. The first time since the establishment of Canada Health Act that an official request from the publicly funded transport services were unable to fulfill all the requests for to offload the over-burdened ICUs to those with available capacity during the COVID-19 pandemic. In partnership with Middlesex London EMS, London Health Sciences Centre (LHSC) established a novel Critical Care Ground Transport (CCGT) team to assist Ornge with the transportation. Case Description: A Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis to assess LHSC’s potential to establish two CCGT teams staffed by critical care register nurse (CCRN), registered respiratory therapist (RRT) and ICU physicians were approved on Apr 15, 2021. April 16–May 7, 2021, Ornge requested twenty-five interfacility transports. Twenty-four were COVID-19 positive and all were mechanically ventilated. Twenty-two patients accepted for transport and three were declined. Five major patient adverse events occurred resulted increasing oxygen requirement and three equipment related incidence did not result any limitations with the transportation. All adverse events occurred early during transport and corrective actions taken following daily debriefings post transport. Conclusions: The principles to establish an expeditionary CCGT team requires a clear mission goal with unwavering support from the institution and senior leadership. CCGT team member needs to have strong clinical, organizational, communication skills; the ability to work in small teams and the ability to thrive in extreme conditions.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".