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Record W2410130866 · doi:10.1177/107110070102200113

Digital Photography in Orthopaedic Surgery

2001· review· en· W2410130866 on OpenAlexaff
Basil Elbeshbeshy, Elly Trepman

Bibliographic record

VenueFoot & Ankle International · 2001
Typereview
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhotographyDigital photographyDigital cameraDigital imagingComputer graphics (images)SoftwareThe InternetMedicineTransparency (behavior)Digital imageComputer scienceMultimediaComputer hardwareComputer visionImage processingVisual artsWorld Wide WebImage (mathematics)

Abstract

fetched live from OpenAlex

Digital photography has become a practical alternative to film photography for documentation, communication, and education about orthopaedic problems and treatment. Digital cameras may be used to document preoperative and postoperative condition, intraoperative findings, and imaging studies. Digital photographs are captured on the charged coupler device (CCD) of the camera, and processed as digital data. Images may be immediately viewed on the liquid crystal display (LCD) screen of the camera and reshot if necessary. Photographic image files may be stored in the camera in a floppy diskette, CompactFlash card, or SmartMedia card, and transferred to a computer. The images may be manipulated using photo-editing software programs, stored on media such as Zip disks or CD-R discs, printed, and incorporated into digital presentations. The digital photographs may be transmitted to others using electronic mail (e-mail) and Internet web sites. Transparency film slides may be converted to digital format and used in digital presentations. Despite the initial expense to obtain the required hardware, major cost savings in film and processing charges may be realized over time compared with film photography.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.006
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.011

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 head0.051
GPT teacher head0.348
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2001
Admission routes1
Has abstractyes

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