General Radiography Examination Statistical analysis of Patients in Geriatric hospitals
Bibliographic record
Abstract
본 연구는 근 골격계 질환을 앓고 있는 60대 이상 노인환자 관리를 위한 기초자료로 삼고자 2012년 1월 1일부터 2012년 12월 31일 지방 소재의 노인요양병원을 내원한 환자 2,500명의 척추 및 상지, 하지, 관절부의 일반 촬영건수 5,042건을 분석하였다. 노인연령은 보로디 연령구분법을 따라 3그룹으로 분류하였다. 병원에 내원한 노인 환자의 대부분이 척추질환과 하지관절질환에 특히 많이 노출되어 있었고. 어깨, 무릎, 요추 촬영 검사 건수가 절반 이상을 차지하고 있으며 공통적으로 연령대별로 1분기 건수가 가장 많았고 2, 3분기에 약간 감소세를 유지하다가 4분기에는 1분기에 비해 감소된 촬영 건수를 나타냈다. 앞으로 더 많은 노인을 대상으로 내원목적과 질환 발생 원인을 분석하고 예방대책을 강구하는 노력이 추가적으로 진행된다면 연구결과를 토대로 노인요양병원의 질환예방교육과 더불어 노인행동 계획을 세우고 농촌지역의 의료자원을 효율적으로 재분배하는데 기초자료로 활용될 것으로 기대된다. The purpose of this study is to establish the basic data for the management of the elderly patients aged more than 60 suffering from musculoskeletal disorders in geriatric hospitals. From January, 1 2012 to December 31, 2012, 2,500 patients who were taken the x-ray inspection and were analyzed x-ray order 5,042 cases above spines, upper lower extremities and joints. The elderly age was divided into 3 groups according to Brody elderly statistics. The majority of elderly patients who visited the hospital are exposed to spinal disorders and joint diseases. Shoulder, knee, L-spine examination cases had accounted for more than half the number of total. By age group, the number of the first quarter was most dominant commonly. Second and third quarter was maintaining slightly decrease. The fourth quarter had reduced compared to the first quarter. If the visiting purposes and the disease causes analysis and preventive measures are taken in a study, it is expected to utilize as basic data to make a plan of the education for preventing diseases and elderly behavior planning for geriatric hospitals and to redistribute health care resources of the rural areas efficiently.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".