Let There Be
Bibliographic record
Abstract
Background: In 2014, the Montreal Neurological Hospital reorganized its stroke unit, grouping together all strokes serviced by Neurology/Neurosurgery to ensure continuity of care. This unprecedented change created a new interdisciplinary /interdepartmental team which required a new form of communication to facilitate information dissemination and patient care in a timely manner. Method: Unlike typical rounds, the purpose of the daily huddle is to briefly set the patients’ goals of the day. The huddles are attended by all interdisciplinary team members, lasting approximately 30 minutes. The Assistant Nurse Manager leads the huddle in the morning in the nursing station to discuss the main issues. The huddle outcomes were assessed by: Length of stay (LOS), turnaround time to implement discharge, patient/family and team satisfaction. Results: Length of stay decreased by 4 days, delay to application to disposition was <24 hours. Interdepartmental team stated satisfaction in sharing their expertise in their different domains. Concerns were expressed if the huddle LOS exceeded 30 minutes. Eighty (80%) percent of patients/ families experienced satisfaction that information provided was given in a caring/timely manner. Conclusion: Daily huddles improved LOS and team learning was enhanced. However, huddles need to be more concise.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.274 | 0.124 |
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".