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Record W2027748878 · doi:10.1089/109264200421577

Laparoscopic Adhesiolysis for Chronic Abdominal Pain: An Objective Assessment

2000· article· en· W2027748878 on OpenAlexaboutno aff
André J. A. Bremers, Jan Ringers, A. Vijn, R. A. J. Janss, Willem A. Bemelman

Bibliographic record

VenueJournal of Laparoendoscopic & Advanced Surgical Techniques · 2000
Typearticle
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLaparotomySurgeryQuality of life (healthcare)Abdominal painAdhesionLaparoscopyChronic painPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Postoperative adhesions frequently occur and can account for various symptoms, including chronic abdominal pain. Conventional adhesiolysis by laparotomy results in an unacceptably high rate of recurrence. A minimally invasive procedure (laparoscopic adhesiolysis) might improve the outcome by inflicting less surgical trauma, but well-documented reports focused on laparoscopic adhesiolysis for chronic abdominal pain are lacking. PATIENTS AND METHODS: Twelve consecutive patients with chronic abdominal pain caused by adhesions who were treated by laparoscopic adhesiolysis were assessed preoperatively and during a 1-year follow-up period applying validated scoring systems: McGill and SLC-90 tests to evaluate personalities and MOS SF-36 and GIQLI questionnaires for the quality of life assessments. RESULTS: No psychological influences were identified. Only two patients experienced a lasting improvement in quality of life, and five patients had more or less stable complaints. Five patients required laparotomy within a year after laparoscopic adhesiolysis. CONCLUSIONS: Laparoscopic adhesiolysis has yet not passed the stage of clinical trial and requires objective evaluation, including detailed information on recurrence and de novo adhesions in correlation with clinical outcome.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.015
GPT teacher head0.348
Teacher spread0.332 · 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.

Study designOther design
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

Citations15
Published2000
Admission routes1
Has abstractyes

Explore more

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