A prospective, descriptive study to assess the reliability and usability of a rapid foot screen for patients with diabetes mellitus in a complex continuing care setting.
Bibliographic record
Abstract
Inlow's 60-second Diabetic Foot Screen is a paper-pencil tool developed to guide professionals in the completion of a quick foot assessment of persons with diabetes mellitus to determine recommended frequency of assessments. The tool has been used in various healthcare settings and its reliability and validity previously tested in acute and long-term care settings. The purpose of this study was to assess content, time to complete assessment, ease of use, and reliability of the tool in a complex continuing care setting. The tool includes questions about 10 variables; skin, nails, deformities, footwear, temperature, range of motion, sensation, pulses, dependent rubor, and erythema. Answers convert to a score ranging from 0 (low risk, yearly screenings) to 23 (high risk, weekly screenings). Using the tool, the study questionnaire, and a watch, three nurse assessors experienced in assessing the feet of persons with diabetes completed 70 assessments on 35 patients during a period of 30 days. Content areas assessed included significance of comorbidities and interval screening times. Mean time to complete the assessment was 7 minutes (range 2 to 21 minutes); 39% of assessments took 6 to 7 minutes. Times to perform assessment varied widely due to the functional and cognitive well-being of the patient. Inter-rater reliability was low (ICC 0.608 [95% confidence interval 0.349-0.781]), perhaps due to varying interpretations of assessment parameters related to the complexity of the study patient population. Comments suggest that some tool revisions may increase ease of use as well as tool validity and reliability, especially for complex care patients with multiple comorbidities.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".