In Response to a Letter to the Editor of the American Journal of Physical Medicine and Rehabilitation
Bibliographic record
Abstract
To the Editor: We thank the authors for the very interesting commentary they have provided in response to our recent article published in the American Journal of Physical Medicine and Rehabilitation: Rehabilitation after critical illness in people with COVID-19 infection. It is heartwarming to hear of the courage and resourcefulness shown by our colleagues in rehabilitation during a time of international crisis, stressful for all concerned, and not without risk. As the authors eloquently describe, rehabilitation has a clear and important role in the emergency response to a pandemic crisis and contributes to evidence-based, quality patient care from admission to an acute hospital unit onward. The need for rapid critical appraisal, intelligent adaptation, and targeted implementation of best practices in the setting of a novel, highly infectious and potentially life-threatening disease provides an opportunity for diverse, sometimes siloed, specialist clinical teams to work together in an extraordinary and innovative fashion. Team working is a core feature in rehabilitation, and this ethos may have prepared the specialty well to mount an effective response to the COVID-19 pandemic. In Canada, we have gained immensely from the rapid knowledge translation of our international colleagues in rehabilitation, who have shared their experiences from the frontline. Locally, we have also clearly seen the appreciation our colleagues in intensive care and acute medicine have for our involvement under such challenging circumstances. Perhaps these reflections cast a positive light on potential future directions for rehabilitation models around the world. A greater emphasis on evidence-based early multidisciplinary assessment and treatment for those with acute severe illness seems to be an achievable goal, clearly to the benefit of the people we serve. A continuum of care needs continuity, and we are in a privileged position to be able to provide this together in our evolving specialty. As an international community, we benefit greatly from a collaborative, collegiate culture, at the best of times, but especially in times of need. Robert Simpson, PhD, MBChBLarry Robinson, MD University of Toronto Sunnybrook Health Sciences Centre Toronto, Ontario, Canada
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.028 | 0.032 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".