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Record W3206374876 · doi:10.53350/pjmhs211592319

Study of Sexual Dimorphism in the Closure of Sagittal Suture – A Postmortem Study

2021· article· en· W3206374876 on OpenAlexaff
Zulfiqar Ali Buzdar, Maryam Shahid, Kanwal Zahra, Muhammad Anwar Sibtain Fazli, Javaid Munir, Zia ul Haq, Fakhar Zaman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsSagittal sutureSexual dimorphismCranial vaultSagittal planeFibrous jointAnatomyAutopsyMedicineClosure (psychology)SkullPathologicalPathologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Performing identity is a prime task in medicolegal and postmortem examinations. Age is the first parameter that has to be determined followed by sex. There are several techniques through which sex can be determined. As well there are different anatomical, physiological and pathological parameters determination of sex. Aim: To determine the sexual dimorphism in the cranial sagittal suture closure macroscopically. Methods: All the cases for this purpose had been selected from those brought for autopsy in the Department of Forensic Medicine and Toxicology, King Edward Medical University Lahore during the year 2016. Results: The statistical analysis revealed early closure in males than in females both ectocranially and endocranially with advancing age in the sagittal suture of cranial vault. The p value was found significant being less than 0.05 thereby establishing the fact that sexual dimorphism in the cranial sagittal suture exists. Conclusion: Conclusively the determination of sex is possible from the pattern of Cranial Sutures closure on autopsy table. Key words: Sex, Sagittal, Suture, Cranial

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)0.001
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0030.000

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 head0.055
GPT teacher head0.324
Teacher spread0.269 · 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
Published2021
Admission routes1
Has abstractyes

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