Epidemiología de la cirugía menor en atención primaria. Estudio descriptivo de 50.000 intervenciones.
Bibliographic record
Abstract
Introduction: Minor surgical (MS) procedures are carried out in different areas of primary care as well as by different professionals. There are not many studies which consistently evaluate these variables, the magnitude of this health benefit, nor its impact on the health system. The aim of the present study is to quantify the magnitude of these techniques in primary care.\nMaterials and methods: Descriptive study of Minor Surgery carried out in 2009 in three primary health-care areas of Asturias with a population of 799,472 inhabitants. \nThe data were obtained from the electronic medical record (OMI-AP).\nResults: 54,723 episodes were found which can be solved by means of either emergency or scheduled MS, a figure that represents 6.8% of the population. \nOf the urgent cases, three-quarters involved healing cutaneous wounds, and among the scheduled cases, a third were benign skin tumours, another third were cutaneous injuries of viral origin, and a quarter were benign subcutaneous tumours.\nThese cases were initially opened by general practitioners (59%) and by nurses (41%). \nConclusions: With regard to previous research, the present study reveals the highest prevalence of cases of minor surgery. Either way, the volume of these surgical interventions is important enough to be taken into account in health services planning, and warrants further research.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".