Use of Geospatial Mapping to Determine Suitable Locations for Patient Service Centers for Phlebotomy Services
Bibliographic record
Abstract
OBJECTIVES: Approaches to determining optimal locations for patient service centers (phlebotomy clinics) have not been addressed in the published literature. Using the city of Calgary, Alberta, Canada, as a test case, our objective is to present a novel method for determining underserviced geographic areas within a city to guide the choice of potential new patient service center locations. METHODS: Data on travel distances for 198,883 phlebotomy visits as well as population data from the 2011 Canada Census were used for this study. Using geospatial mapping techniques, we produced maps of the city showing actual relative travel distances for patients as well as the geographic distribution of population density of patients undergoing phlebotomies. RESULTS: There was a striking pattern of increased travel distances in certain parts of the city. These also corresponded to geographic areas with greater density of patients seeking phlebotomies. CONCLUSIONS: This analysis provided clear, objective evidence of communities that are currently relatively underserved by patient service centers. This approach could be used by other laboratories to plan the location of new patient service centers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".