Bibliographic record
Abstract
Au Québec, la Durée Moyenne de Séjour (DMS) des patients dans les départements d’urgence dépasse 16 heures; une situation inacceptable par les patients, les équipes médicales et les administrateurs des hôpitaux. Ce phénomène d’encombrement des urgences est attribuable à divers facteurs souvent externes à ces départements. Afin de réduire le DMS aux urgences, cette thèse propose quatre contributions majeures. La première contribution consiste dans le développement de modèles de prévision des demandes de soins et d’hospitalisation selon un regroupement des patients par Catégories Majeures de Diagnostique (CMD). La deuxième contribution est la définition d’une nomenclature stochastique selon un regroupement des patients. Cette nomenclature permet de convertir la demande de soin dès l’étape de triage en besoin de ressources nécessaires pour répondre à cette demande. La troisième et la quatrième contribution sont l’analyse de l’effet du concept des cellules dynamiques appliqué aux salles d’examen et du Fast Track externe sur des indicateurs de performance du département d’urgence. Cette analyse utilise la modélisation simulatoire conjointement à des plans d’expérience. Cette thèse a contribué à l’avancement de la recherche par deux articles de revue avec comité de lecture et deux présentations de conférence avec comité de lecture. \n \nIn Quebec, the average length of stay of patients in emergency departments exceeds 16 hours; an unacceptable situation for patients, medical teams and hospital \nadministrators. This situation of overcrowding in emergency departments is attributed to various factors often external to these departments. In order to reduce the Length of stay in emergency departments, this thesis proposes four major innovations. The first innovation consists in the development of models for forecasting the emergency department visits and hospitalization according to a grouping of patients by Major Category of Diagnostic. The second innovation is the definition of a stochastic Bill Of Resources according to a grouping of patients. This Bill Of Resources converts the demand for care at the triage stage into a need of the resources necessary to \nmeet the demand of care. The third and fourth innovation is the analysis of the effect of the dynamic cell concept applied to examination rooms and the external Fast Track on emergency department performance indicators. This analysis uses simulation tools in conjunction with experimental designs. This thesis contributed to the advancement of the research by two refereed journal articles and two refereed conference presentations.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".