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Record W2021783020 · doi:10.3143/geriatrics.47.302

National study on acceptance by Japanese nursing homes of patients with feeding tubes

2010· article· en· W2021783020 on OpenAlexaboutno aff
Yayoi Takezako, Eiji Kajii

Bibliographic record

VenueNippon Ronen Igakkai Zasshi Japanese Journal of Geriatrics · 2010
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNursing homesNursingFeeding tubeOdds ratioConfidence intervalQuarter (Canadian coin)Internal medicineSurgery

Abstract

fetched live from OpenAlex

AIM: This study describes the acceptance of patients with feeding tubes in nursing homes for the elderly. METHODS: We sent questionnaires to 1,438 nursing homes in 2006-7 asking how many patients with feeding tubes the nursing homes had and how many new patients with feeding tubes they would accept in the future. RESULTS: The response rate was 63.6%. We analyzed the data of 735 nursing homes. The median range (25-75%) of the number of patients, patients accepted, and total number of patients currently resident was determined. The percentage of tube feedings to total beds in those categories was 8.0% (range 4.0-13.3), 5.0% (0-10.0), and 13.3% (8.0-23.8), respectively. Whereas 6% of the nursing homes had no limits on acceptance of patients with feeding tubes, 27.2% of the nursing homes replied that they would no longer accept such patients. Factors associated with restricted acceptance included nurse responses (odds ratio (OR) 0.54, 95% confidence interval (CI) 0.30-0.95), a facility with over 100 beds (OR 2.14, 95% CI 1.10-4.17), and no current patients with feeding tubes (OR 4.19, 95% CI 2.36-7.43). CONCLUSION: One quarter of nursing homes for the elderly in Japan replied that they would no longer accept patients with feeding tubes. More nurses than other professionals replied that they would accept patients with feeding tubes. Larger nursing homes were less likely to accept tube-feeing patients. Furthermore, nursing homes with no tube-feeding patients were unwilling to accept such 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.073
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.294
Teacher spread0.281 · 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 teacher head, 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

Citations5
Published2010
Admission routes1
Has abstractyes

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