m-Health in the Surgical Context: Prospecting, Review and Analysis of Mobile Applications
Bibliographic record
Abstract
Background m-Health initiatives can show an opportunity to improve the identification, prevention and management of certain diseases. Most health-care applications aims at chronic noncommunicable diseases care, and it is necessary to seek evidence from applications intended for surgical patient care, either before, during or after hospitalization. Objective: The Study Aims to identify and analyze, from the literature review and prospecting, applications for smartphones developed to assist the surgical patient. Methods: A descriptive-exploratory study developed in two sequential phases. The first one corresponded to an integrative review of the literature in the databases PubMed, Web of Knowledge, Europe PMC database and Cumulative Index to Nursing and Allied Health Literature database. The second phase corresponded to an exploration of applications in the European Patent Office, United States Patent and Trademark Office (USPTO), Free Patents Online, Canadian Intellectual Property Office and World Intellectual Property Organization. In both phases, we do not restrict the results by year of publication/registration or language. At each stage, the selected studies/patents were analyzed and pre-selected, according to the inclusion and exclusion criteria, by reading their titles and abstracts. Subsequently, we analyzed those with the potential of participation in the study, evaluating the answer to the research question, as well as the type of research, objectives, sample, method, outcomes, results and conclusion. Finally, the articles/patents record found were read in full. Results In the databases, 14 studies that presented some application for smartphone aimed at the surgical patient were selected. Most of them were in PubMed (64.3%), published in English (100%). Regarding origin, 28.6% of the texts are from the United States of America, 14.3 from Sweden and 14.3 from Canada. On the other hand, in patent databases, 10 registries were selected, 60% in the USPTO patent base, hosted in hybrid systems (iPhone and Android) and developed in the last 5 years (2014-1018) (80%). In general, there is a series of applications aimed at surgical patients, such as targets and/or users, mainly focused on the exchange of text and image messages but concentrated on assisting the physician/health team in the preparation of the patient during the procedure or post discharge. It is necessary to invest in the creation of technologies that aim to monitor these patients, especially in post discharge. Conclusion The characteristics of applications indicate a strong hospital centered tendency in relation to its purpose, having the surgeon as the main user and the surgical patient as the main target. The applications are still focused on optimizing diagnostics or functions, and no initiatives are identified to monitor the patient in a non-hospital environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".