Bibliographic record
Abstract
Alasan tersering pasien mencari pertolongan medis adalah nyeri. Nyeri merupakan pengalaman sensoris dan emosional tidak menyenangkan yang berhubungan atau digambarkan berkaitan dengan kerusakan jaringan atau organ. Pengendalian nyeri optimal memerlukan tim penanganan nyeri yang terorganisasi, pengetahuan pasien, pelatihan, pendidikan yang terus menerus, penggunaan analgesik multimodal, dan pemeriksaan derajat nyeri yang seragam. Penilaian dampak utama penanganan nyeri meliputi: tingkat rasa nyeri, efek samping terapi, frekuensi penggunaan analgetik, saat pasien pulang, dan tingkat kecemasan. Proyek “Menuju pain-free hospital” pertama kali diperkenalkan di St. Luc Hospital, Montreal (Kanada) pada tahun 1992. Tujuan proyek ini adalah untuk memperkenalkan dan mempertahankan standar analgesik post-operatif tertinggi. Salah satu elemen kunci proyek ini adalah pendidikan berkelanjutan. Masyarakat dan pasien harus disadarkan atas kemungkinan dan pentingnya penanganan nyeri, perlunya kerja sama dengan para petugas medis dan hak mereka agar nyerinya diobati. Pain in one of the most common reason to seek medical attention. The optimal control of pain requires an organized pain management team, patient education, training and lifelong learning, use of multimodal analgesia, and uniformity of pain severity examination. The assessment of pain management include: level of pain, side effects from therapy, frequency of analgesics use, patient discharge time, and level of anxiety. A project called Towards a pain-free hospital was first introduced in St. Luc Hospital, Montreal (Canada) in 1992. The purpose of this project is to introduce and maintain the highest standard postoperative analgesia. The main element of this project is continuing education. Patients and public should be aware on the importance of pain management, the need for cooperation with medics and their right to be treated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.268 | 0.070 |
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".