| Course | HCR 400 Evidence-Based Practice for the Health Care Professional |
|---|---|
| Module | Module 2 |
| Paper type | Critical appraisal of a diagnostic accuracy study |
| Length | About 515 words, 4 pages |
| Format | APA 7 student paper |
| School | Arizona State University |
| Program | BS in Health Care Coordination |
| Updated | October 2026 |
Free sample paper for HCR 400 Module 2
Rapid Critical Appraisal: A Deep Learning Algorithm for Detecting Diabetic Retinopathy
Student Name
BS in Health Care Coordination, Arizona State University
HCR 400: Evidence-Based Practice for the Health Care Professional
Instructor Name
Month Day, Year
Rapid Critical Appraisal: A Deep Learning Algorithm for Detecting Diabetic Retinopathy
Study and Question
Study: Gulshan et al. (2016), development and validation of a deep learning algorithm for referable diabetic retinopathy.
Clinical question: In adults with diabetes having retinal photographs (P), how accurately does the algorithm (I) identify referable diabetic retinopathy compared with grading by ophthalmologists (C, reference standard), measured by sensitivity and specificity (O)?
Are the Results Valid?
The strongest feature is the rigorous reference standard: majority grades from multiple ophthalmologists. The main limitation is spectrum: validation sets came from screening programs with specific cameras, and ungradable images were excluded from the main analysis, so performance in a busy clinic with poorer images is uncertain (Hoffmann et al., 2023).
| Appraisal question | Answer | Notes |
|---|---|---|
| Was there an independent comparison with a reference standard? | Yes | Images graded by at least seven board-certified ophthalmologists |
| Was the reference standard applied to all images? | Yes | All validation images graded |
| Did the sample include an appropriate spectrum of patients? | Partly | Screening images from the U.S. and France; image quality and populations may differ from other clinics |
| Was the test developed separately from validation? | Yes | Trained on about 128,000 images; validated on two separate sets |
| Were the test and reference interpreted independently? | Yes | Algorithm grades produced without access to the reference grades |
What Are the Results?
In the larger validation set, EyePACS-1, with 9,963 images and a 7.8 percent prevalence of referable retinopathy, the algorithm's area under the curve was 0.991. At a high-specificity setting, sensitivity was 90.3 percent and specificity 98.1 percent; at a high-sensitivity setting, sensitivity was 97.5 percent and specificity 93.4 percent (Gulshan et al., 2016).
Predictive values at 7.8 percent prevalence, high-sensitivity setting, per 1,000 images:
Positive predictive value is about 76 of 137, or 55 percent; negative predictive value is about 861 of 863, over 99 percent. For a screening program, a very high negative predictive value means a negative result can safely spare most patients a specialist visit, while about half of positive results will be confirmed by an eye specialist.
| Disease present (78) | Disease absent (922) | |
|---|---|---|
| Test positive | 76 | 61 |
| Test negative | 2 | 861 |
Will the Results Help My Patients?
For a primary care clinic serving many people with diabetes who miss eye examinations, an accurate automated reader could expand screening. But the study measured accuracy, not outcomes: it did not show that using the algorithm leads to earlier treatment or less vision loss. Local image quality, the camera used, workflow for referrals and patient acceptance would need evaluation before adoption. The same gap between measuring well and improving care appears elsewhere in primary care; home blood pressure monitoring, for example, lowers pressure mainly when someone acts on the readings (Uhlig et al., 2013).
Appraisal Summary
| Domain | Judgment |
|---|---|
| Validity | Strong reference standard; spectrum concerns |
| Results | Very high accuracy; excellent negative predictive value |
| Applicability | Promising for screening; outcome and local validation needed |
Conclusion
The study provides strong evidence of diagnostic accuracy under research conditions. It supports piloting automated retinal screening, with local validation and a clear referral pathway, rather than immediate replacement of specialist grading.
References
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P. C., Mega, J. L., & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410. https://doi.org/10.1001/jama.2016.17216
Hoffmann, T., Bennett, S., & Del Mar, C. (2023). Evidence-based practice across the health professions (4th ed.). Elsevier.
Uhlig, K., Patel, K., Ip, S., Kitsios, G. D., & Balk, E. M. (2013). Self-measured blood pressure monitoring in the management of hypertension: A systematic review and meta-analysis. Annals of Internal Medicine, 159(3), 185-194. https://doi.org/10.7326/0003-4819-159-3-201308060-00008
HCR 400 Module 2 instructions, in plain terms
Of the 85 points HCR 400 gives to appraisals, 15 go to the diagnosis study. A diagnostic accuracy study asks how well a test separates people who have a condition from people who do not, so the questions shift away from randomization toward the quality of the comparison standard, the mix of patients tested and the way accuracy is reported. The textbook's chapter on diagnostic evidence introduces sensitivity, specificity, predictive values and likelihood ratios and explains how to judge each. Some instructors hand out the study; others let groups choose one linked to their topic, and the Canvas page will say which applies this term. Whatever the source, show at least one calculation from the study's own figures, because numbers you derive yourself demonstrate that you understand what the accuracy measures mean.
How this HCR 400 Module 2 example is built
Built as a rapid appraisal, the sample starts with the study and a structured clinical question naming the population, the index test, the reference standard and the accuracy outcomes. Its validity table covers the reference standard, whether every image received it, the spectrum of patients, separation of development and validation data and independent interpretation, and the paragraph beneath identifies the panel of ophthalmologists as the decisive strength and image spectrum as the decisive weakness. Results are reported at both operating points, and a two-by-two table built at the study's own prevalence turns sensitivity and specificity into predictive values. The applicability section draws a firm line between accuracy and patient outcomes, and a summary table closes the work.
Reading the HCR 400 Module 2 grading rubric
Fifteen points are on offer for the diagnosis appraisal. Credit usually follows a careful look at the reference standard, comment on whether the patients tested resemble those in practice, accurate reporting of sensitivity and specificity with their operating points, a correct calculation of predictive values or likelihood ratios at a stated prevalence and recognition that a highly accurate test still has to prove it improves care. Points are commonly lost when predictive values are treated as fixed properties of a test, when the reference standard is never examined, when ungradable or excluded cases go unmentioned and when the conclusion recommends adoption without considering workflow or local validation.
HCR 400 Module 2 help from the desk
Before writing, sketch a two-by-two grid with true and false positives and negatives, filling it from the study's prevalence and accuracy figures; predictive values then fall out of simple division. Always state the prevalence your calculation assumes, since a test that looks excellent in a specialist clinic can produce mostly false alarms in general screening. Compare the study's patients and images with those your setting would produce. Keep accuracy and outcomes in separate paragraphs so the distinction is obvious to your grader. If sensitivity and specificity still feel slippery, the desk can build a grid with you using your study's numbers.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Arizona State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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HCR 400 Module 2 questions, answered
Where can I find a free HCR 400 Module 2 sample paper?
Scroll up for the complete HCR 400 Module 2 sample, which appraises a diagnostic study of an AI reader for diabetic eye disease.
What is the difference between sensitivity and specificity?
Sensitivity is the share of people with the condition who test positive; specificity is the share without it who test negative.
Why do predictive values depend on prevalence?
With few true cases in the tested group, even a small false positive rate can outnumber real detections, so a positive result means less.
What is a reference standard in a diagnostic study?
The best available method for deciding who truly has the condition, against which the new test is compared.
How much is the HCR 400 diagnosis appraisal worth?
It is worth 15 points.