Telemedicine, diabetes and endocrinologic diseases in the COVID-19 era: the patients’ point of view from a high impacted area. (Preprint)
Bibliographic record
Abstract
BACKGROUND The effectiveness of telemedicine has been widely investigated, also in a diabetologic setting. Available evidence support an important role of telemedicine in emergency times, like the present COVID-19 outbreak. OBJECTIVE The aim of the study is to evaluate the feasibility and patient satisfaction associated with virtual visit use in an outpatient diabetology and endocrinology unit in Pavia, Italy, during the present COVID-19 pandemic. METHODS We submitted a custom-made satisfaction questionnaire to each patient who underwent a televisit from the 1st of April to the 1st of May 2020. The questionnaire was proposed to 51 subjects. Furthermore, the patients were asked about their gender, age and visit reason. RESULTS All patients accepted to answer our questionnaire. Their mean age was of 38 years (minimum 20, maximum 79) and most of patients were women (39). The patients resulted affected by different disease: type 1, type 2 and gestational diabetes, hypothyroidism during pregnancy, Graves’ disease, Klinefelter Syndrome, Acromegaly, Cushing syndrome and hypopituitarism. The majority of them didn’t find any difficulty in using the informatic system, recognized a value of telemedicine in preventing COVID-19 contagion, and considered the tele-visit useful. CONCLUSIONS Our results show that patients were overall satisfied of this new approach. Telemedicine, also in a diabetologic and endocrinologic setting, can be of help in providing continuing healthcare, while keeping health providers and patients safe during COVID-19 pandemic. CLINICALTRIAL
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".