MétaCan
Menu
← Back to cohort
Record W4307554432 · doi:10.30683/1929-2279.2022.11.08

Effect of Risk-Graded Care Based on Caprini Risk Assessment Model on Postoperative Venous Thrombosis and Health-Related Quality of Life in Elderly Patients with Malignancy

2022· article· en· W4307554432 on OpenAlexvenueno aff
Yafang He, Li Wang, Yingfen Zhang

Bibliographic record

VenueJournal of cancer research updates · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDeep veinVenous thrombosisThrombosisMalignancyPsychological interventionIncidence (geometry)GeriatricsNursing Interventions ClassificationNursing careGrading (engineering)SurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

Objective: To investigate the effect of risk-graded care based on Caprini risk assessment model on postoperative deep vein thrombosis and quality of life in elderly patients with malignant tumors. Methods: Sixty-eight elderly patients with malignant tumors treated by surgery admitted to the Department of Geriatrics of the First Affiliated Hospital of Sun Yat-sen University from April 2021 to September 2021 were selected to be included in the control group and given routine nursing interventions in the geriatrics department, using the Autar deep vein thrombosis risk scale and cluster nursing interventions; Seventy cases of elderly patients with malignant tumors treated by surgery admitted to our geriatric department from October 2021 to 2022 were included in the observation group, and the risk-grading nursing intervention based on the Caprini risk assessment model was used in the observation group on the basis of conventional nursing interventions. The number of cases of deep vein thrombosis, D-II cluster values, average hospitalization days, nursing satisfaction, and quality of life levels were compared between the two groups. Results: The number of VTE cases and the incidence of VTE in the observation group was 0.43%(3/70) lower than that in the control group (0.89%(6/68)); the average hospital stay in the observation group (13.50+7.45) was lower than that in the control group (15.16+10.60) and the D-II aggregation value in the observation group (2.90+4.32) was lower than that in the control group (4.02+3.91); with statistically significant differences (P<0.05); the satisfaction scores of communication, safety, guidance, nursing, and nursing techniques in the observation group were (41.70+4.21), (48.53+5.12), (38.47+1.90), (56.77+3.33), (47.80+1.68) points, higher than those in the control group (30.11+8.57), (41.69+7.95), (31.75+6.95), (46.87+7.31), (36.0+9.0) points, with statistically significant differences (t-values of -10.634 to -2.404, all P<0.05); the post-intervention scores of general health, physical function, physical function, somatic pain, somatic energy, social function, emotional function, mental health, and spiritual change dimensions of quality of life in the observation group were (67.00+14.95), (65.71+25.24), (63.21+21.59), (83.63+10.65), (74.43+13.45), (70.71+20.95), (67.62+26.60), (62.60+15.12) and (76.79+20.11) and scores were higher than the control group's post-intervention scores of (57.50+19.65), (40.44+27.33), (43.01+25.86), (54.57+15.42), (42.65+20.08), (56.25+26.67), (41.18+28.87), (52.35+18.86), (66.91+23.83) scores than the control group after intervention, with statistically significant differences (t-values of -26.878 to 0.989. all P<0.05). Conclusions: Risk-graded nursing intervention based on the Caprini risk assessment model can effectively reduce the incidence of postoperative VTE in elderly patients with malignancy, improve nursing satisfaction, and enhance the quality of life of 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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.113
GPT teacher head0.501
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of cancer research updates→Same topicCOVID-19 and healthcare impacts→French-language works237,207→