Development of an evidence-based specialty support surface decision tool.
Bibliographic record
Abstract
Advancing technology, an aging population, increasing attention to appropriate resource use, and growing concerns about patient safety and professional liability combine to complicate support surface choice. Cognizant of these factors, staff in a 600-bed tertiary care hospital in a large urban center in western Canada decided to evaluate an existing specialty support surface decision tool and update the instrument based on current published literature (no older than 3 years), expert opinion, and results of pilot testing. Elements included in the existing tool were the patient's Braden Score, mobility/activity indicators, and identification of existing skin breakdown. The tool allowed considerable latitude in decision-making based on other clinical factors and established professional practices and had not been formally evaluated. The revised tool addressed relevant assessment criteria such as risk category, patient weight, presence of existing skin breakdown/number of ulcers, flap surgery, pulmonary complications, palliative care, positioning, and Braden score, as well as oversight of support service choice. Although the incidence of nosocomial pressure ulcers did not change significantly during the trial period, costs incurred for support surface use decreased 26% overall, underscoring the need for improved guidelines for support surface selection.
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 | 0.057 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".