Reply to Fenton <i>et al.</i> : An Expanded COVID-19 Telemedicine Intermediate Care Model Using Repurposed Hotel Rooms
Bibliographic record
Abstract
To date, our jurisdiction has had great success with social distancing, self-isolation, and robust contact tracing in containing COVID-19.At the time of writing this, we have had a total of 962 cases with 15 deaths and enjoy an effective reproductive number of approximately 2.3 (recently increased from ,1.0 because of a localized and contained outbreak).Fortunately, to date, our hospitals have not been overwhelmed like some of our Canadian and international peers.As such, we have not yet had to activate this model of care.We highly appreciate the data presented by our Italian colleagues that supports the feasibility of the convalescent part of our model.It remains to be seen how our multientry point model performs relative to theirs.In summary, we applaud Bruni and colleagues (1) on their work and present a similar but more comprehensive hotel-based model of COVID-19 care.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.039 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".