Étude Descriptive du Processus D’ÉValuation et de Documentation de la Douleur Postopératoire dans un Hôpital Universitaire
Bibliographic record
Abstract
Several studies have shown that patients often receive inadequate treatment of postoperative pain. The aim of the present descriptive study was to examine and analyze various data related to the postoperative pain assessment of 40 patients who underwent elective surgery. Pain journals were to be completed by patients during every waking hour for the first three postoperative days to assess both pain intensity and pain unpleasantness. A post hoc analysis of patient records permitted verification of pain assessment by nurses for each patient. The results showed that not only was postoperative pain rarely assessed using a valid scale, it was also poorly documented. In addition, when nurses assessed and documented postoperative pain using a numerical scale, their results were very different from patients' assessments. For the first postoperative day, the mean (± SD) pain intensity documented by nurses on a 0 to 10 numerical scale was 1.57±0.23, while the mean pain intensity noted by patients using the same scale was 3.82±0.41. Statistical analysis showed that there was no significant correlation between mean pain intensity documented by nurses and the mean pain intensity noted by patients.
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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".