Wounds and Survival in Noncancer Patients
Bibliographic record
Abstract
BACKGROUND: Currently available prognostic models for noncancer patients lack high levels of discrimination. Therefore, the quest for additional prognostic factors must continue. To date, none have utilized the occurrence of wounds as a prognostic factor. METHODS: As a prospective observational study, based on a sequential case series of 189 advanced noncancer patients, all wounds were documented. One hundred seventy patients were followed until their deaths. Univariate and multivariate survival analyses were performed using hazard ratios (HRs) derived from Cox proportional hazard models. RESULTS: Seventy-eight percent of patients presented with at least one wound at referral. Patients with wounds displayed worse overall survival than those without wounds (p = 0.009). Survival analysis for the full postreferral period revealed a violation of the proportional hazards assumption for pressure ulcers and the Palliative Performance Scale version 2 (PPSv2). In order to address this finding, early deaths (within 14 days of referral) were assessed separately from later deaths (more than 14 days after referral). After controlling for the co-occurrence of wounds, age, gender, Charlson comorbidity index, and PPSv2, pressure ulcers were associated with statistically significant increased risk of death for patients of sufficient health to survive at least 2 weeks after referral (HR 2.42, p = 0.003), while other wounds were associated with greater levels of mortality over the full postreferral period (HR 1.96, p = 0.0001). DISCUSSION: The occurrence of pressure ulcers and other wounds are correlated with reduced survival in patients with advanced noncancer illness. These data merit incorporation into existing prognostic models or used in conjunction with them to enhance prognostic accuracy.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".