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Record W4404017939 · doi:10.29309/tpmj/2024.31.11.8335

Frequency and outcomes of parathyroid preservation in total thyroidectomy.

2024· article· en· W4404017939 on OpenAlexaff
Rashid Ahmad, Spogmay Sammer, Muhammad Idrees, Waseem Ahmad Jadoon

Bibliographic record

VenueThe Professional Medical Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsMedicineTotal thyroidectomyThyroidectomyGeneral surgeryInternal medicineThyroid

Abstract

fetched live from OpenAlex

Objective: To determine the frequency and its outcome of parathyroid preservation in total thyroidectomy. Study Design: Case Series study. Setting: Department of Otorhinolaryngology, HMC, MTI, Peshawar. Period: 23rd June 2022 to 23rd Dec 2022. Methods: A total of 246 patients who underwent thyroidectomy were included in the study and followed up to determine the preservation of parathyroid gland, hypocalcaemia and recurrent laryngeal nerve injury. Results: The mean age of the sample was 37.7 + 12.3 years. There were 52.8% male patients and 47.2% female patients. 28.9% of patients had thyroid nodule and subtotal thyroidectomy was the most performed procedure Parathyroid gland preservation was recorded in 72%. On follow up, hypocalcemia was recorded in 16.3% and recurrent laryngeal nerve injury in 20.3%. Conclusion: Hypocalcemia and RLNI are significantly common after thyroidectomy. Hypocalcemia was significantly high in patients with low non preserved parathyroid gland. More research on high sample size and addressing other effect modifiers are recommended with intervention to preserve parathyroid gland.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.333
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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