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Record W2018916171 · doi:10.1002/jso.22139

The quality of life trajectory of resected gastric cancer

2011· article· en· W2018916171 on OpenAlexafffund
Gitonga Munene, Wesley Francis, Sheila N. Garland, Guy Pelletier, Lloyd A. Mack, Oliver F. Bathe

Bibliographic record

VenueJournal of Surgical Oncology · 2011
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsUniversity of Calgary
FundersHealth Research BoardAlberta Cancer Board
KeywordsMedicineGastrectomyQuality of life (healthcare)CancerSurgeryInternal medicineDiseaseGastroenterology

Abstract

fetched live from OpenAlex

BACKGROUND: Few studies describe quality of life (QoL) outcomes following gastrectomy for gastric cancer using a validated instrument. The gastric cancer module for the Functional Assessment of Cancer Therapy system of QoL measurement tools (FACT-Ga) was utilized to determine the changes in QoL following gastrectomy, and during the disease course. METHODS: In 43 patients undergoing gastrectomy for gastric cancer, outcome such as complications, recurrence, and survival were annotated. Karnofsky performance status (KPS) and QoL were determined preoperatively and at each follow-up visit. RESULTS: Nineteen (44%) patients and 24 (56%) patients underwent partial gastrectomy (PG) and total gastrectomy (TG), respectively. Complications occurred in 30%, and one mortality (2.3%) occurred. Median survival was 23 months. KPS, FACT-G, and FACT-Ga scores all decreased after surgery, and normalized by 6 months. There was no significant difference in QoL in patients who had a PG or TG, although the type of gastrectomy did affect KPS. QoL dropped on average 4.4 ± 3.6 months prior to death. CONCLUSIONS: Surgery adversely affects QoL for up to 6 months. Thereafter, QoL mirrors changes in disease status. More studies are required to document the QoL cost-benefit ratio in gastric cancer, which often is accompanied by short survival benefits.

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)0.000
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 score0.153
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.109
GPT teacher head0.382
Teacher spread0.274 · 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

Citations36
Published2011
Admission routes2
Has abstractyes

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