Dermatology practice in primary health care services: where do we stand in the Middle East?
Bibliographic record
Abstract
BACKGROUND: There has been a distinct expansion of the primary health care services in the Middle East over the past two decades. As a consequence, the exposure of primary care physicians (PCPs) to skin disorders has increased. However, information is lacking regarding the level of proficiency of PCPs in this field. OBJECTIVE: The purpose of our study is to assess the ability of the primary care physicians, with or without training in dermatology, to identify, diagnose and manage skin disorders. MATERIAL AND METHODS: Physicians at university-hospital primary-care clinics were asked to answer a multiple-choice questionnaire regarding various dermatoses. These were grouped into: common, infrequent and rare. Questions included identification of the correct description of the skin lesion, diagnosis, treatment and the desirability of referral. Demographic characteristics of the physicians were also assessed. RESULTS: Nineteen PCPs were included. The eight PCPs who had had specific training in dermatology showed performance superior to that of the PCPs who did not (P = 0.04). Not surprisingly, PCPs were able to make the correct diagnosis more frequently for the common dermatoses than for the infrequent or rare dermatoses (P = 0.001). On the other hand, when asked to recognize a correct description of the skin lesion, the PCPs were most often correct with rare dermatoses, and least often correct with common dermatoses (P = 0.04). CONCLUSION: PCPs with a short period of specific clinical training in dermatology perform better in identifying, diagnosing and managing skin disorders than those without. Such training for PCPs should be considered to provide more effective delivery of health care.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".