Evaluation of a tele-expertise experiment for skin cancer detection: the perspective of GPs
Bibliographic record
Abstract
Abstract Background The incidence of skin cancer has shown an increasing trend in the world and in France over the last four decades. In this context, the delay to access a specialist opinion strongly impacts the patient outcomes. However, the medical demographics of dermatologists in France have been decreasing over the past years. Hence, a tele-expertise (TLE) experiment for skin tumors detection, which allows general practitioners to obtain feedback from a dermatologist within 7 days using a smartphone application, has been set up in the Hauts-de-France region since 2015. Thus, this study aims at understanding the advantages and drawbacks of TLE in the detection of skin tumors perceived by general practitioners. Methods Exploratory study carried out with 15 general practitioners participating in the experiment in the Hauts-de-France region. Semi-structured interviews were conducted between february and april 2021 and analyzed after transcription. Results The main advantages perceived by general practitioners are: (i) fast access to a specialized feedback; (ii) formalization of the request and commitment of the dermatologist; (iii) ease of regulating patients to a specialist; (iv) lower travel frequency and stress for patients; (v) ability to send medical information securely. The drawbacks are most related to the lack of functionalities and the graphical user interface of the application. Conclusions Tele-expertise is a well accepted technological innovation in dermatology among general practitioners, which enables the best use of the scarce medical resources available in the region and to address the needs of patients. The sustainability of TLE in dermatology and its extension to other territories is possible and desirable. Key messages Tele-expertise in dermatology has its place in enabling the early management of malignant skin tumors. The qualitative approach makes it possible to understand general practitioners visions after the introduction of a technological innovation into their practices.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".