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In order to identify endoscopy codes from each data source appropriately, a literature review was conducted and input from local physicians was obtained.

Since our purpose was to identify all lower gastrointestinal endoscopies regardless of purpose, all codes that indicated use of an endoscope were included.

Primary and secondary data sources for endoscopy were collected from the Alberta Cancer Registry, cancer medical charts and three different administrative data sources.

1672 randomly sampled patients diagnosed with invasive colorectal cancer in years 2000–2005 in Alberta, Canada were included.

A retrospective validation study of administrative data for endoscopy in the year prior to colorectal cancer diagnosis was conducted.

A gold standard dataset was created by combining all the datasets.

Cancer medical charts are initially created for all patients diagnosed with cancer by the Alberta Cancer Registry for use in coding cases.

Here is the comparison: gala[1:3,]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Species Endemics\u00a0 Area Elevation Nearest Scruz Adjacent\n Baltra\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 58\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 23 25.09\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 346\u00a0\u00a0\u00a0\u00a0 0.6\u00a0\u00a0 0.6\u00a0\u00a0\u00a0\u00a0 1.84\n Bartolome\u00a0\u00a0\u00a0\u00a0\u00a0 31\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 21\u00a0 1.24\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 109\u00a0\u00a0\u00a0\u00a0 0.6\u00a0 26.3\u00a0\u00a0 572.33\n Caldwell\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 3\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 3\u00a0 0.21\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 114\u00a0\u00a0\u00a0\u00a0 2.8\u00a0 58.7\u00a0\u00a0\u00a0\u00a0 0.78\n gala2[1:3,]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Species Endemics\u00a0 Area Elevation Nearest Scruz Adjacent c1\n Baltra\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 58\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 23 \u00a0 \u00a0 \u00a025.09\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 346\u00a0\u00a0\u00a0\u00a0 0.6 \u00a0 \u00a0 \u00a0 \u00a00.6 \u00a0 \u00a0 \u00a0 \u00a01.84 \u00a0 \u00a0 \u00a0 1\n Bartolome \u00a0 31\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 21 \u00a0 \u00a0 1.24\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 109 \u00a0 \u00a0 \u00a0 0.6 \u00a0 \u00a0 \u00a026.3 \u00a0 \u00a0 572.33 \u00a0 \u00a0 \u00a02\n summ1\n[1] 259520.5\n The do-it-yourself approach allows us to incorporate any computation we like inside the leave-one-out loop.The following data were abstracted from the charts: date and type of endoscopy; result (cancer, suspicious, not cancer); and source of information (letter, dictation notes, report).Endoscopy data were obtained from three provincial administrative databases, the first two of which conform to national reporting standards: 1) the Discharge Abstract Database (hospital inpatient data) which records information on all admissions to hospitals in Alberta; 2) the Ambulatory Care Classification System Database (hospital outpatient data), which contains information on all outpatient visits that occurred in hospitals, such as visits to hospital-based physicians’ offices, hospital endoscopy units, and emergency departments; and 3) the Physician Billing database, which contains all billing claims submitted by physicians remunerated on a fee-for-service basis and “shadow” billing submitted by physicians employed through the Alternate Relationship Plan (ARP).On the other hand, cross validation, by allowing us to have cases in our testing set that are different from the cases in our training set, inherently offers protection against overfittting. Do-it-yourself leave-one-out cross validation in R. In this type of validation, one case in our data set is used as the test set, while the remaining cases are used as the training set.We iterate through the data set, until all cases have served as the test set.

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