Bibliographic record
Abstract
The operating room (OR) within a hospital environment is one of the most \nexpensive functional areas, yet the use of the OR also provides hospitals with an \nessential source of income. However, at present, there are variations on how to \nevaluate the performance of ORs, since there is no clear and full explanation of \nthe concept and methods used for evaluation. \nThe overall aim of this thesis is to develop an evidence based Operating Room \nAssessment Framework (ORAF) to evaluate Operating Room performance with \nclear and complete guidelines that can be used by operating room managers, \ndirectors or any other medical professionals to evaluate operating room \nperformance, determine OR planning and scheduling efficiency, OR workload \nand OR utilization. The resulting Operating Room Assessment Framework will \nassist targeted healthcare professionals in their quest to evaluate, monitor and \nimprove overall Operating Room efficiency. \nThe OR management systems of eight tertiary and teaching hospitals in three \ncountries (Japan, Canada and Saudi Arabia) have been examined from 2010 to \n2012, which include more than 98,500 procedures. \nThe Operating Room Assessment Framework (ORAF) involves three important \nelements of Operating Room performance, namely: OR scheduling level, the type \nof OR workload, and OR utilization. These elements can simply be read to reach \nthe end result, which includes three types of scheduling levels: under scheduling, \nideal scheduling and over scheduling; five types of OR workload: OR total \nworkload (the gross workload), OR actual workload, over workload, unnecessary \nworkload and unexpected workload; and three types of OR utilization: \nunderutilization, ideal utilization, and 100% utilization with over workload. \nThrough the validation process in different hospital contexts, the ORAF has \nproven its ability to perform satisfactorily, with accuracy, in line within the \nresearch’s objectives.
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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 | 0.066 | 0.187 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".