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Record W1512230728 · doi:10.1159/000155898

Quality of Life following Colorectal Cancer Surgery: The Role of Alexithymia

2008· article· en· W1512230728 on OpenAlexaboutno aff
V. Ripetti, Fabio Ausania, Roberto Bruni, G. Campoli, Roberto Coppola

Bibliographic record

VenueEuropean Surgical Research · 2008
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaToronto Alexithymia ScaleMedicineColorectal cancerQuality of life (healthcare)Prospective cohort studyInternal medicineCancerSurgeryClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Alexithymia refers to a set of cognitive and emotional deficits. Its effect on surgical outcome has been demonstrated but no studies have been published on colorectal cancer patients. STUDY DESIGN: A series of 60 consecutive colorectal cancer patients were enrolled in a 3-year prospective study on quality of life by using the SF-36 test and Toronto Alexithymia Scale questionnaires. Patients were investigated pre- and postoperatively (before discharge and then 1 and 3 months thereafter). The control group consisted of patients undergoing laparoscopic cholecystectomy for cholelithiasis. These two groups were divided into two subsets: high-level alexithymia (HA) and low-level alexithymia (LA). The prevalence of HA was 34% in colorectal patients and 35% in cholelithiasis patients. RESULTS: During the postoperative period, in the colorectal group the SF-36 score was significantly higher in HA than in LA subsets. This result was confirmed in the cholelithiasis group. During follow-up, a progressive reduction of the SF-36 score was observed in both HA populations. DISCUSSION: Results emerging from this investigation demonstrate that surgery significantly improves the quality of life in HA patients. These findings suggest that alexithymia might be advantageous in evaluating the adaptation after surgery in the short follow-up period.

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)0.003
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 score0.143
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.132
GPT teacher head0.406
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

Citations17
Published2008
Admission routes1
Has abstractyes

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