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EVALUATION OF USE OF ANALGESICS IN PAIN MANAGEMENT AMONG SURGEONS IN A TERTIARY CARE HOSPITAL

2018· article· en· W2898633272 on OpenAlexaboutno aff
Jayshree Dawane, Kalyani Khade, Yamini Ingale, Vijaya Pandit

Bibliographic record

VenueAsian Journal of Pharmaceutical and Clinical Research · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsnot available
FundersIndian Council of Medical Research
KeywordsMedicineMedical prescriptionTramadolVisual analogue scaleAnalgesicTertiary careAnesthesiaPhysical therapyEmergency medicineNursing

Abstract

fetched live from OpenAlex

Objective: The objective of this study is to evaluate pain and to assess if analgesic prescriptions are according to the World Health Organization guidelines. Methods: The study was conducted in the Department of Surgery in a tertiary care hospital. Patients with age >18 years, of either sex, admitted to surgery ward were included in the study. Pain assessment was done using a visual analog scale and McGill questionnaire. Information obtained from case paper sheets was recorded, such as name of analgesics, the generic name of prescribed analgesics, dosage, route of administration, frequency, number of analgesics per prescription, and non-pharmacological techniques. Data generated from the questionnaire were entered into an Excel sheet, and percentages were calculated. Results: A total of eight different analgesics were prescribed in the study group. Paracetamol was the maximally prescribed drug (40%). In 48% of cases, antacids were given along with analgesics. A majority of analgesics were prescribed in generic names (52%). No drug was prescribed to almost 18% cases even though the pain intensity was of mild-to-moderate intensity. Conclusion: Commonly prescribed drugs were paracetamol + tramadol. Prescription pattern of analgesics is partially deviating from standard guidelines. Generic names were written in the majority of prescriptions, which is in accordance with standard prescription writing.

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)0.014
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.168
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.153
GPT teacher head0.479
Teacher spread0.326 · 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 teacher head, 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

Citations1
Published2018
Admission routes1
Has abstractyes

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