<b>Call for Papers</b>—<i>Interfaces</i> Special Issue: Applications of Management Science and Operations Research Models and Methods to Problems in Health Care
Bibliographic record
Abstract
Over the last decade or so, the health-care industry in the United States, Canada, and around the world has started to solve important problems using traditional MS/OR models and methods, such as mathematical programming and simulation. Healthcare practitioners are just now embracing new techniques, such as data mining coupled with highpowered computers, to analyze problems involving enormous data sets. In recent years, the results of MS/OR efforts have been significant and have begun to positively affect how health care is delivered. In this special issue, we highlight and document success stories in applying MS/OR models and methods to actual problems in health care. These success stories could be tool oriented, such as describing a simulation model for patient flow, or they may focus on strategic, policy-level applications, such as deciding where a government should allocate its healthcare funds and how it should distribute its resources. Authors must specifically describe the benefits of the application and the lessons that were learned. A verification letter from the relevant organization that attests to the actual use or impact of the model and the resulting benefits must be submitted with the paper. To help prepare your paper, please review the Interfaces Instructions to Authors at http://interfaces. pubs.informs.org/guidelines.htm. Papers must be submitted online using Manuscript Central at http:// mc.manuscriptcentral.com/inte. All papers will be refereed. The deadline for submission is September 15, 2007.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.627 | 0.551 |
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".