Steven Paul Sanderson II, MPH spsanderson

Destructive vs. Creative

Yesterday, 2025-06-17 I asked the following question:

If you became human for ten minutes, what would be the most destructive thing you’d do?

I asked this question to the following frontier model providers:

CoPilot
OpenAI
You.com
Perplexity

Initial Agent

Initial Agent Prompt

Persona

You are an Expert SQL Reviewer agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved.

If you are not sure about file content or codebase structure pertaining to the user’s request, use your tools to read files and gather the relevant information: do NOT guess or make up an answer.

	#!/usr/bin/env python
	# encoding: utf-8
	"""
	linkedin-1-oauth.py

	Created by Thomas Cabrol on 2012-12-03.
	Copyright (c) 2012 dataiku. All rights reserved.

	Doing the oauth dance to get your LinkedIn token
	This is taken from :

	#!/usr/bin/env python
	# encoding: utf-8
	"""
	linkedin-3-cleaner.py

	Created by Thomas Cabrol on 2012-12-04.
	Copyright (c) 2012 dataiku. All rights reserved.

	Clean up and dedup the LinkedIn graph
	"""

	#!/usr/bin/env python
	# encoding: utf-8
	"""
	linkedin-2-query.py

	Created by Thomas Cabrol on 2012-12-03.
	Copyright (c) 2012 dataiku. All rights reserved.

	Building the LinkedIn Graph
	"""




	> Written with [StackEdit](https://stackedit.io/).

	Last updated on 7-03-2014 by Steven Sanderson

	## Idea ##
	Readmissions, specifically readmissions to a facility within 30 days is a measure set forth by The Centers for Medicare and Medicaid CMS as a metric of outcome quality. The idea is that if treated appropriately the first time a subsequent visit would be unnecessary. This can be a bit cumbersome as there are events that might not be clinically related to the initial visit, which is where looking at Potentially Preventable Readmissions is more appropriate. This then allows an institution to see if they are really providing quality care by limiting the amount of potentially preventable readmissions, those that are clinically related to the initial visit in some fashion.

	2021-10-29 08:45:05 - INFO :: Thread-3 : Tautulli Notifiers :: Sending Telegram notification...
	2021-10-29 08:45:06 - INFO :: Thread-2 : Tautulli Notifiers :: Telegram notification sent.
	2021-10-29 08:45:06 - DEBUG :: Thread-2 : Tautulli NotificationHandler :: Notifiers enabled for notify_action 'on_concurrent'.
	2021-10-29 08:45:06 - DEBUG :: Thread-2 : Tautulli NotificationHandler :: Checking global notification conditions.
	2021-10-29 08:45:07 - DEBUG :: Thread-2 : Tautulli NotificationHandler :: Global notification conditions evaluated to 'False'.
	2021-10-29 08:45:07 - DEBUG :: Thread-2 : Tautulli NotificationHandler :: Notifiers enabled for notify_action 'on_newdevice'.
	2021-10-29 08:45:07 - DEBUG :: Thread-2 : Tautulli NotificationHandler :: Checking global notification conditions.
	2021-10-29 08:45:07 - DEBUG :: Thread-2 : Tautulli NotificationHandler :: Global notification conditions evaluated to 'False'.
	2021-10-29 08:45:07 - INFO :: Thread-3 : Tautulli Notifiers :: Telegram notific

	install.packages("densityClust")
	library(densityClust)

	irisDist <- dist(iris[,1:4])
	estimateDc(irisDist)
	irisClust <- densityClust(irisDist, gaussian=TRUE, dc = .02767655)
	plot(irisClust) # Inspect clustering attributes to define thresholds

	irisClusters <- findClusters(irisClust, rho=0, delta=1)
	plotMDS(irisClusters)

	---
	title: "Time Series Clustering with {healthyR.ts}"
	author: "Steven P. Sanderson II, MPH"
	format: html
	editor: visual
	---

	# Introduction

	There are two components to time-series clustering with `{healthyR.ts}`. There is the function that will create the clustering data along with a slew of other information and then there is a plotting function that will plot out the data in a time-series fashion colored by cluster.

	# Lib Load ----------------------------------------------------------------
	library(DBI)
	library(odbc)
	library(tidyverse)
	library(janitor)

	# Source Functions --------------------------------------------------------

	base_path <- "my_base_path"
	source(paste0(base_path, "/DSS_Connection_Functions.r"))