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Record W2024687074 · doi:10.2310/7070.2001.20897

Carbon Dioxide Laser Microsurgery for Tongue Cancer: Surgical Techniques and Long-Term Results

2001· article· en· W2024687074 on OpenAlexvenueno aff
Ching-Ping Wang, Shyue‐Yih Chang, Jen-De Wu, Shyh‐Kuan Tai

Bibliographic record

VenueThe Journal of Otolaryngology · 2001
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryCarbon dioxide laserTongueMicrosurgeryDebulkingRadiation therapyTransoral laser microsurgerySurgical marginFree flapStage (stratigraphy)CancerLaser surgeryHead and neck cancerOvarian cancerResectionLaserPathology

Abstract

fetched live from OpenAlex

Thirty-seven consecutive patients with cancer of the anterior two-thirds of the tongue without clinical neck lymph nodes or distant metastasis were treated with transoral carbon dioxide (CO2) laser microsurgery. During the operation, a monopolar coagulation-suction device was applied to achieve a clear surgical field. Under a surgical microscope, we repeatedly palpated the soft tissue to identify the tumour margins, in particular the deep margin, to maintain adequate safe margins in three dimensions. We resected the tumour by en bloc procedures rather than by vaporization or debulking. Of the 28 patients in the T1 and T2 groups, 26 patients did not receive postoperative radiotherapy. The local control rate calculated by the methods of Kaplan and Meier in all 37 patients at 5 years was 93.6%. No local recurrence occurred in the T1 or T2 cases. Nine patients suffered from neck recurrence and the neck control rate at 5 years was 74.6%. Eight of these nine patients were salvaged by surgery with adjuvant radiotherapy, and six of them finally achieved disease-free status. The 5-year disease-free survival rate for our series was 88%. Our surgical techniques using CO2 laser microsurgery are effective and advantageous methods for excision of oral tongue cancer, especially stage I and II lesions.

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 score0.170
Threshold uncertainty score0.310

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)0.0000.000

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 head0.025
GPT teacher head0.321
Teacher spread0.297 · 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

Citations20
Published2001
Admission routes1
Has abstractyes

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