Bibliographic record
Abstract
Cleveland, OH, Delos M. "Toby" Cosgrove, MD, has overseen that institution's transformation, in his words, "from a doctor-centric organization to a patient-problem-centric organization." Once following the traditional hospital model in which departments of surgery and medicine were separate and medical specialists in one organ system or disease state might never speak to colleagues specializing in another, Cleveland Clinic recently embarked upon a major eff ort to organize its clinical staff into broad institutes encompassing closely related organ systems and diseases.Th is approach, Dr. Cosgrove explains, can improve medical care for the individual patient and foster a collaborative environment among clinicians that leads to discovery, innovation, and ultimately, enhanced treatments and better outcomes for patient populations.It was a "novel idea" to "think a hospital should be organized around patients instead of around doctors, " observes Dr. Cosgrove.Th at idea developed from a sense that the traditional organizational model did not take full advantage of the resources a hospital has at its disposal.As a cardiac surgeon in a department of surgery, Dr. Cosgrove met formally with other types of surgeonsneurosurgeons or colorectal surgeons, for example-with whom, he says, "I had almost nothing in common except that we used operating rooms and wore gloves." In contrast, he explains, "I had everything in common with the cardiologists: we shared patients, we shared disease problems . . .gradually the lines between surgery and medicine were blurring."Th e unorthodoxy of a hospital in which cardiac surgeons would share departments with cardiologists was not unwelcome to many members of the Cleveland Clinic community who could imagine some of the potential benefi ts such a restructuring would bring.Dr. Cosgrove notes, "Nobody came and said, 'Th is is a crazy idea.Th is will never work.'" He acknowledges, however, that there was signifi cant concern over the logistical details of the plan, with many of the clinical staff left to wonder, "Who am I going to report to?Where's my offi ce going to be?" Certainly these were legitimate questions: dissolving the traditional medical and surgery departments meant rethinking organizational hierarchies and relocating specialists in related treatment areas so that, for example, the neurosurgeons, neurologists, and psychiatrists could be housed together in their own institute.Despite clear challenges that would
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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 | 0.069 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.051 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".