Intuitive Surgical Inc. Payments to Ob/Gyns Compared to Other Specialties: Analysis from 2014 Open Payments Database [4D]
Bibliographic record
Abstract
INTRODUCTION: Study Objective: To determine the payments made to Obstetricians and Gynecologists (OBGYNs) compared to other surgical specialties by the makers of the Da Vinci robot-Intuitive Surgical, Inc. in 2014. METHODS: Design: Retrospective cross-sectional study (Canadian Task force classification- II-3). Setting: Payments to OBGYNs in the United States compared to the total Intuitive Surgical payment amount in each state as analyzed from Centers for Medicare & Medicaid Services (CMS)’ Open Payments website. Subjects: General OBGYNs, OBGYN subspecialists (Gynecologic Oncology, Reproductive Endocrinology, Female Pelvic Medicine and Reconstructive Surgery, Gynecology), and providers in other specialties nationwide. Interventions: Compare Intuitive Surgical Inc. payments to OBGYNs versus other surgical specialties as analyzed from publicly available CMS Open Payments database. RESULTS: A total of 50 states received money from Intuitive Surgical Inc. in 2014. Payments to general OBGYNs totaled $5,061,439, OBGYN subspecialties received $1,591,556, and other surgical specialties that use the Da Vinci platform received $33,011,657. Total payments per state can be represented in a map diagram. The state of Texas got the highest amount. Payments to OBGYNs divided by total payments in each state can be depicted in a similar fashion, Wyoming state had the highest value. CONCLUSION: OBGYNs received a significant proportion of funding from Intuitive Surgical Inc. as compared to other surgical specialties in 2014. We recommend that OBGYNs update or declare any conflicts when required.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".