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Record W4362717086 · doi:10.1111/codi.16561

Abdominoperineal resection by the trans‐anal <scp>total mesorectal excision</scp> approach: are we refuting the technology a bit too early?

2023· letter· en· W4362717086 on OpenAlexaboutno aff
Prudvi Raj, Swapnil Patel, Durgatosh Pandey

Bibliographic record

VenueColorectal Disease · 2023
Typeletter
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAbdominoperineal resectionTotal mesorectal excisionResectionBit (key)SurgeryColorectal cancerInternal medicineComputer networkCancer

Abstract

fetched live from OpenAlex

We read with interest the recent publication on the comparison of outcomes of low anterior resection (LAR) and abdominoperineal resection (APR) using trans-anal total mesorectal excision (taTME) by the Canadian taTME Expert Collaboration [1]. This retrospective analysis highlights the inferior outcomes of patients in the APR group, cautioning against the use of taTME for the same. However, we feel a careful consideration of the following observations might enhance future research on the subject. In general, patients with disease below the pelvic floor (planned for APR) have biologically more aggressive cancers compared to disease above the pelvic floor (planned for LAR). This in combination with technical difficulties with the procedure translates into inferior oncological outcomes for patients undergoing APR. In the current study, the APR subgroup had more advanced tumours (53% Stage III tumours) compared to the LAR subgroup (25% Stage I tumours). There is no description of grade of histological differentiation of the tumours in the two subgroups. There were more patients of higher body mass index in the APR subgroup, possibly associated with technically difficult procedural conduct. The anterior quadrant is the most susceptible for positive circumferential resection margin (CRM) in the traditional technique of APR as emphasized by the MERCURY II study [2]. This part of perineal dissection is often done under compromised vision in the standard technique, possibly accounting for the increased rates of involved CRM and specimen perforations. As per the technique of taTME APR described by the authors, the anterior dissection of the rectum from prostate or vagina is done under direct vision. A pathological description of involved CRM with regard to involved quadrant of specimen in patients with positive CRM would shed more light on the pathophysiology of proposed inferior oncological outcomes of taTME APR. Two technical advancements which have been applied in the previous decade to improve the surgical approach for low rectal cancers have been the use of a robotic platform and the use of taTME. Shin et al. [3] have shown that careful application of both these techniques leads to superior quality of TME, even in ‘technically challenging or high-risk patients’ (tumour height less than 4 cm, male narrow pelvis, body mass index more than 25 kg/m2). While the learning curve for the robotic approach has been standardized globally, the same is needed for the taTME platforms. A previous report on inferior outcomes following taTME by the Norwegian group attributes the poor outcomes to the pneumo-perineum resulting in increased tumour dissemination and subsequently increased characteristic local recurrence patterns [4]. More clarity on the patterns of recurrence would help identify the technical failures, if any. Future studies validating the role of taTME in low rectal cancers need to specifically recruit and analyse the subgroup of patients at high risk of suboptimal surgery using a traditional approach. Comparison in a homogeneous cohort planned for the same surgery (APR) would be more prudent. Results from the COLOR III trial would shed more light on the role of trans-anal procedures in comparison to standard laparoscopic procedures [5]. Author roles: Conceptualisation: Swapnil Patel, PrudviRaj Data duration.: Swapnil Patel Formal analysis: Prudvi Raj Methodology: Durgatosh Pandey Supervision: Durgatosh Pandey Writing original draft: PrudviRaj Writing- reviewing: Swapnil Patel. None. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
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 head0.032
GPT teacher head0.276
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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