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Record W4322506881 · doi:10.31080/asms.2023.07.1483

Considerations in Medical Academic Writings

2023· article· en· W4322506881 on OpenAlexaff
Iqbal Ratnani, Saleem A Zaid, Anam Mithwani, Justyn Mahanger, Salim Surani

Bibliographic record

VenueActa Scientific Medical Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsPsychologyHistory

Abstract

fetched live from OpenAlex

This paper provides an overview on how to write an academic writing piece with a step-by-step process for completion, as expected in academia, and is intended to support the crafting of scientific papers, written by physicians, nurses, and other allied healthcare personnel in the broader medical field.This article is written to guide new writers through the essential considerations for writing scientific manuscripts, such as identifying the targeted audience, purpose, and devising a strategy before starting a written piece, as well as incorporating organizational layout, flow, style, and presentation.

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)0.218
metaresearch head score (Gemma)0.428
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score0.964

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2180.428
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.003
Science and technology studies0.0180.029
Scholarly communication0.0330.021
Open science0.0050.018
Research integrity0.0110.021
Insufficient payload (model declined to judge)0.0100.011

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 head0.090
GPT teacher head0.428
Teacher spread0.338 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreMethods

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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