Analysis of Secure Apps for Daily Clinical Use by German Orthopedic Surgeons: Searching for the "Needle in a Haystack"
Bibliographic record
Abstract
BACKGROUND: It is undeniable that appropriate smartphone apps offer enormous opportunities for dealing with future challenges in orthopedic surgery and public health, in general. However, it is still unclear how the apps currently available in the two major app stores can be used in daily clinical routine by German orthopedic surgeons. OBJECTIVE: This study aimed to gain evidence regarding the quantity and quality of apps available in the two major app stores and their suitability for use by orthopedic surgeons in Germany. METHODS: We conducted a systematic, keyword-based app store screening to obtain evidence concerning the quantity and quality of commercially available apps. Apps that met the inclusion criteria were evaluated using the app synopsis-checklist for users and the German Mobile App Rating Scale for secure use, trustworthiness, and quality. RESULTS: The investigation revealed serious shortcomings regarding legal and medical aspects. Furthermore, most apps turned out to be useless and unsuitable for the clinical field of application (4242/4249, 99.84%). Finally, 7 trustworthy and high-quality apps (7/4249, 0.16%) offering secure usage in the daily clinical routine of orthopedists were identified. These apps mainly focused on education (5/7). None of them were CE (Conformité Européenne) certified. Moreover, there are no studies providing evidence that these apps have any positive use whatsoever. CONCLUSIONS: The data obtained in our study suggest that the number of trustworthy and high-quality apps on offer is extremely low. Nowadays, finding appropriate apps in the fast-moving, complex, dynamic, and rudimentarily controlled app stores is most challenging. Promising approaches, for example, systematic app store screenings, app-rating developments, reviews or app libraries, and the creation of consistent standards have been established. However, further efforts are necessary to ensure that these innovative mobile health apps not only provide the correct information but are also safe to use in daily clinical practice.
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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 | 0.018 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".